Patterns of cognitive function in aging: The Rotterdam Study

Abstract

Cognitive impairment is an important hallmark of dementia, but deterioration of cognition also occurs frequently in non-demented elderly individuals. In more than 3,000 non-demented persons, aged 45-99 years, from the population-based Rotterdam Study we studied cross-sectional age effects on cognitive function across various domains. All participants underwent an extensive cognitive test battery that tapped into processing speed, executive function, verbal fluency, verbal recall and recognition, visuospatial ability and fine motor skills. General cognitive function was assessed by the g-factor, which was derived from principal component analysis and captured 49.2 % of all variance in cognition. We found strongest associations for age with g-factor [difference in z-score -0.59 per 10 years; 95 % confidence interval (CI) -0.62 to -0.56], fine motor skill (-0.53 per 10 years; 95 % CI -0.56 to -0.50), processing speed (-0.49 per 10 years; 95 % CI -0.51 to -0.46), and visuospatial ability (-0.48 per 10 years; 95 % CI -0.51 to -0.45). In contrast, the effect size for the association between age and immediate recall was only -0.25 per 10 years (95 % CI -0.28 to -0.22), which was significantly smaller than the relation between age and fine motor skill (P < 0.001). In conclusion, in non-demented persons of 45 years and older, general cognition deteriorates with aging. More specifically, fine motor skill, processing speed and visuospatial ability, but not memory, are affected most by age.

Full text

NEUROEPIDEMIOLOGY
Patterns of cognitive function in aging: the Rotterdam Study
Yoo Young Hoogendam •Albert Hofman •
Jos N. van der Geest •Aad van der Lugt •
Mohammad Arfan Ikram
Received: 2 September 2013 / Accepted: 11 February 2014 / Published online: 20 February 2014
ÓSpringer Science+Business Media Dordrecht 2014
Abstract Cognitive impairment is an important hallmark
of dementia, but deterioration of cognition also occurs
frequently in non-demented elderly individuals. In more
than 3,000 non-demented persons, aged 45–99 years, from
the population-based Rotterdam Study we studied cross-
sectional age effects on cognitive function across various
domains. All participants underwent an extensive cognitive
test battery that tapped into processing speed, executive
function, verbal fluency, verbal recall and recognition,
visuospatial ability and fine motor skills. General cognitive
function was assessed by the g-factor, which was derived
from principal component analysis and captured 49.2 % of
all variance in cognition. We found strongest associations
for age with g-factor [difference in z-score -0.59 per
10 years; 95 % confidence interval (CI) -0.62 to -0.56],
fine motor skill (-0.53 per 10 years; 95 % CI -0.56 to
-0.50), processing speed (-0.49 per 10 years; 95 % CI
-0.51 to -0.46), and visuospatial ability (-0.48 per
10 years; 95 % CI -0.51 to -0.45). In contrast, the effect
size for the association between age and immediate recall
was only -0.25 per 10 years (95 % CI -0.28 to -0.22),
which was significantly smaller than the relation between
age and fine motor skill (P\0.001). In conclusion, in non-
demented persons of 45 years and older, general cognition
deteriorates with aging. More specifically, fine motor skill,
processing speed and visuospatial ability, but not memory,
are affected most by age.
Keywords Aging Cognitive function Cohort 
G-factor Population-based Dementia
Introduction
Normal aging, as well as various clinical diseases, such as
for example dementia, are accompanied by a deterioration
of cognitive function. Even though memory decline is a
hallmark of dementia, other cognitive domains, like exec-
utive function and processing speed are also often affected
[1]. Many studies focus on persons in pre-clinical stages of
dementia, i.e. mild cognitive impairment, and therefore are
not always generalizable to community-dwelling elderly
[2–4]. Still, cognitive aging has also been investigated
extensively outside the context of dementia. Age effects
have been documented on several cognitive domains, such
as spatial orientation, inductive reasoning, memory, verbal
and number skills, and in a variety of populations [5,6].
However, different rates of cognitive decline across cohorts
have also been reported and age effects on cognition could
be altered over time due to changes in a population with
regard to, for example, education, environment, health
Electronic supplementary material The online version of this
article (doi:10.1007/s10654-014-9885-4) contains supplementary
material, which is available to authorized users.
Y. Y. Hoogendam A. Hofman M. A. Ikram (&)
Department of Epidemiology, Erasmus MC University Medical
Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
e-mail: m.a.ikram@erasmusmc.nl
Y. Y. Hoogendam A. van der Lugt M. A. Ikram
Department of Radiology, Erasmus MC University Medical
Center, Rotterdam, The Netherlands
J. N. van der Geest
Department of Neuroscience, Erasmus MC University Medical
Center, Rotterdam, The Netherlands
M. A. Ikram
Department of Neurology, Erasmus MC University Medical
Center, Rotterdam, The Netherlands
123
Eur J Epidemiol (2014) 29:133–140
DOI 10.1007/s10654-014-9885-4

factors, or employment [7–9]. Therefore, more contempo-
rary data on aging effects on cognition are needed.
In order to gain a comprehensive understanding of
cognitive function in non-demented elderly, it is essential
to study a broad range of cognitive domains in unselected
community-dwelling persons. Furthermore, in addition to
studying separate domains, it is equally important to
investigate global cognition. The rationale for this is that
cognition consists of a general underlying construct that is
domain-independent and reflects an individual’s general
cognitive function. This construct is linked to intelligence
and can be quantified as a general cognitive factor, or
g-factor. The g-factor is a stable concept, comprising the
shared variance between cognitive tests, and can be inter-
preted as a common underlying factor to a variety of
cognitive domains [10–12]. The g-factor has even been
shown to be independent of cognitive test batteries used,
and can therefore be easily generalized to other studies
[13].
The aim of this study was to investigate patterns of
cognitive function in middle-aged and elderly community-
dwelling persons. We specifically studied both general
cognition, using the g-factor, as well as specific cognitive
domains.
Methods
Setting
The study is embedded within the Rotterdam Study, a
population-based cohort study in middle-aged and elderly
participants that started in 1990 and aims to investigate
frequency, causes and determinants of chronic diseases
[14]. The initial cohort encompassed 7,983 persons and
was expanded by 3,011 persons in 2000 and by 3,932
persons in 2005. In-persons examinations take place every
3–4 years and consists of home interview and three center
visits. The institutional review board of Erasmus MC
approved the study and participants gave written informed
consent.
Study population
Table 1shows the number of participants from each cohort
used in this study. Also, age at time of invitation to the
study, sex and if available level of education are given for
participants and non-participants to the study (Supple-
mentary table 1). Additionally, we show age and sex of
participants by year (Supplementary Table 2). The current
cross-sectional study focuses on the period from January
1st 2008 onwards, because only then the full cognitive test
battery in its current format was implemented. From the
persons who responded to the invitation to participate in
the study (n =7,963), persons with a stroke (n =325) or
prevalent dementia (n =73) were excluded from the
sample used in this study. Sixteen persons had both a stroke
and dementia and were excluded. For dementia, the
assessment is based on a two-step procedure, which has
been published before [15]. It involves screening by mini-
mental state examination (MMSE), additional work-up by
CAMDEX, informant interview, additional neuropsycho-
logical assessment, imaging, and final diagnosis in a con-
sensus meeting led by a neurologist. For stroke, the
Table 1 Participation to the
current study presented per
cohort
Note that for cohort RSIII-1
there is a remarkably large
difference between the amount
of persons that participated in
the interview and the amount of
persons that participated in any
of the cognitive tests. This large
difference can be explained by
the fact that the included sample
(n =1,132) was selected from
the point at which the design
organization test was included
in the study. This test was only
fully introduced into the
Rotterdam Study in January
2008. Sex and mean age are
based on the number of
participants to any cognitive test
(n =4,422)
RSIII-1 RS-II-3 RS-I-5 Total
Time period of invitations for
participation
Jan 2008–
Feb 2012
Dec 2008–
Sep 2011
Dec 2008–Nov 2010 Jan 2008–
Feb 2012
Females (%) 55.7 56.3 59.9 57.5
Mean age in years
(standard deviation)
60.0 (8.1) 72.4 (5.2) 79.5 (4.8) 71.9 (9.7)
Total number of living
persons invited for the
current study
6,027 2,322 2,952 11,301
Refusal 1,074 344 597 2,015
Incapable to participate 10 38 92 140
Incapable to participate due to
self-reported dementia
02562 87
Non-response 1,017 23 56 1,096
Total number of responders to
invitation and interview
3,926 1,892 2,145 7,963
Number of participants to any
cognitive test
1,132 1,639 1,651 4,422
Number of participants to all
cognitive tests
764 1,189 1,068 3,021
134 Y. Y. Hoogendam et al.
123

assessment is based on self-report, family doctor files, and
files of medical specialists, which are all discussed in a
consensus panel led by a neurologist [16]. Also, neuroim-
aging is used if required.
Until February 29th 2012, cognitive tests were per-
formed in 3,706 up to 4,176 persons. In case of technical
problems, refusal of participation, physical limitations, or
deviation from instructions, test results were excluded.
This explains the range in number of subjects that per-
formed various cognitive tests. The number of persons in
the study who completed a valid cognitive test result on
any of the tests used was 4,422 (Table 1). The complete
cognitive test battery was available in 3,021 persons.
Cognitive test battery
During two separate center visits a cognitive test battery
was administered, which included MMSE [17], Stroop test
[18], letter-digit substitution task (LDST) [19], verbal flu-
ency test [20], 15-word verbal learning test (15-WLT) [21],
design organization test (DOT) [22] and Purdue pegboard
test [23]. A description of the cognitive tests, test demands
and latent skills measured is given in Table 2. Level of
education was obtained and categorized into seven levels,
ranging from primary to university education. Higher
scores indicate a better performance on all cognitive tests,
except for the Stroop task in which a higher score indicates
a worse performance. Scores for the Stroop task were thus
inverted for better comparison to other tests. The DOT is a
test which is based on and highly correlated to WAIS-III
block design, but is administered in two rather than 10 min
and is less dependent on motor skills than the block design
test [22]. Test score on the DOT has a range from 0 to 56
points for each subject.
G-factor [12]
To calculate a general cognitive factor (g-factor) we per-
formed a principal component analysis incorporating color-
word interference subtask of the Stroop test, LDST, verbal
fluency test, delayed recall score of the 15-WLT, DOT and
Purdue pegboard test. For tests with multiple subtasks we
chose only one subtask in order to prevent highly corre-
lated tasks distorting the factor loadings. Principal com-
ponent analysis was performed on complete case data of
3,021 persons. The g-factor was identified as the first
Table 2 Description of
cognitive tests Cognitive test Test demand Latent skills
Mini mental state examination [17] 30 Item test (range 0–30) Global cognitive function
Stroop task [18]
Reading subtask Reading color names aloud (time
taken)
Speed of reading
Color naming subtask Naming colors (time taken) Speed of color naming
Color-word interference subtask Naming colors of color names
printed in incongruous ink color
(time taken)
Interference of automated
processing and attention
Letter-digit substitution task [19] Writing down numbers underneath
corresponding letters (range
0–125)
Processing speed, executive
function
Verbal fluency test [20] Mentioning as many animals
possible in 1 min
Efficiency of searching in long-
term memory
15-Word learning test [21]
Immediate recall Immediate recall of 15 words
directly after visual presentation
(range 0–15)
Verbal learning
Delayed recall Delayed recall of words 10 min
after visual presentation (range
0–15)
Retrieval from verbal memory
Recognition Correctly recognize words that
were shown 10 min before
(range 0–15)
Recognition of verbal memory
Design organization test [22] Reproduce designs using a
numerical code key (range 0–56)
Visuospatial ability
Purdue pegboard both hands [23] In 30 s, place as many pins in
parallel rows of holes using left
and right hand simultaneously
(range 0–25)
Dexterity and fine motor skill
The Rotterdam Study 135
123

unrotated component of the principal component analysis
and explained 49.2 % of all variance in the cognitive tests.
This is a typical amount of variance accounted for by the
g-factor [12].
Statistical analysis
To aid comparison across cognitive tests we first calculated
z-scores for cognitive test scores. The MMSE score was
not standardized due to its skewed nature. We used analysis
of covariance to compare scores between men and women,
adjusting for level of education. We used linear regression
models to investigate the continuous association between
age and cognitive test score, corrected for level of educa-
tion. In additional analyses we used subcohort as an extra
covariate to the linear regression model to test for cohort
effects. We used Z tests to formally test differences of age
effects between cognitive tests. We tested interaction
effects between age and sex and explored non-linear effects
of age on cognition. All analyses were performed using the
statistical software package SPSS version 20.0 for Win-
dows. Results are presented with 95 % confidence intervals
(CI).
Results
Mean age was 71.9 years (SD =9.7), with 57.5 % women
(Table 3). Men scored better than women on the DOT,
whereas women scored better on Stroop color naming,
immediate recall, delayed recall and recognition parts of
the 15-WLT, and Purdue pegboard test (Table 3). Pearson
correlation coefficients between all cognitive test scores are
shown in Supplementary table 3.
Figure 1illustrates MMSE score and g-factor in 5-year
strata of age. MMSE score stayed stable until age 70 and
then showed a rapid decline. In contrast, the g-factor
showed decline in scores already from age 45 onwards. The
mean decline in g-factor per 10 year increase in age was
-0.59 (95 % CI -0.62 to -0.56). For both MMSE score and
g-factor we also found a quadratic effect of age (Table 4).
Figure 2shows mean test scores in 5-year strata of age.
We found the strongest decline for Purdue pegboard,
LDST, DOT and Stroop interference task (Table 5). In
contrast, smaller effects of age were found for 15-WLT
immediate recall (-0.25 per 10 years; 95 % CI -0.28 to
-0.22), delayed recall (-0.23 per 10 years; 95 % CI
-0.26 to -0.20) and recognition (-0.09 per 10 years;
95 % CI -0.12 to -0.05). These differences in age effects
between the memory subtasks versus Purdue pegboard,
LDST, DOT and Stroop were confirmed by formal statis-
tical testing (Z tests). For example, the age effects on the
Purdue pegboard test or DOT were both significantly larger
than the effect on immediate recall (P\0.001). Still, the
strongest effects of age were on the g-factor, rather than
any individual cognitive test.
Finally, we found that for the Purdue pegboard test and
LDST, age effects were stronger in women than men. Also,
quadratic effects of age on cognition were found for the
Stroop tasks, the LDST, verbal fluency, and the Purdue
pegboard test. Adding subcohort as an extra covariate to the
model did not reduce the effects of age on cognitive scores.
Discussion
In a large community-dwelling cohort of persons
45 years and older, we found that age strongly affects
Table 3 Characteristics of the study population
Men
(n =1,880)
Women
(n =2,542)
Pvalue
sex
difference*
Age, years 71.5 ±9.5 72.2 ±9.8 0.02
Primary education only
(%)
9.5 16.8 \0.01
Cognitive tests
Mini mental state
examination, test
score
27.6 ±2.3 27.6 ±2.2 0.02
Stroop reading subtask,
seconds
17.8 ±3.7 18.0 ±3.7 0.97
Stroop color naming
subtask, seconds
24.8 ±5.4 24.2 ±4.9 \0.01
Stroop interference
subtask, seconds
54.3 ±20.2 54.4 ±21.3 0.11
Letter digit substitution
task, number of correct
digits
27.6 ±6.8 27.7 ±7.7 \0.05
Verbal fluency test,
number of animals
21.8 ±5.7 21.2 ±5.9 0.46
15-Word learning test
immediate recall,
number of correct
answers
20.8 ±6.0 22.9 ±6.3 \0.01
15-Word learning test
delayed recall, number
of correct answers
6.5 ±2.7 7.5 ±2.9 \0.01
15-Word learning test
recognition, number of
correct answers
13.0 ±2.1 13.5 ±1.9 \0.01
Design organization test,
number of corrects
25.6 ±9.8 23.0 ±10.3 \0.05
Purdue pegboard test,
number of pins placed
9.4 ±1.8 9.9 ±1.8 \0.01
Values are unadjusted mean ±standard deviation
*Pvalues for cognitive tests comparing values of men and women
are adjusted for level of education
136 Y. Y. Hoogendam et al.
123

general cognitive function, measured by the g-factor. The
effect of age on general cognitive function was already
apparent from 45 years onwards. Investigating separate
cognitive domains, we found strongest associations of
age with fine motor skill, processing speed, and visuo-
spatial ability.
MMSE
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Score
26
27
28
29 g-factor
A
g
e per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Fig. 1 Age effects on global
cognitive scores. The x-axis
represents age per 5 years and
the y-axis represents the
MMSE-score or z-score of the
g-factor. Error bars represent
95 % confidence intervals.
Estimates are adjusted for level
of education. MMSE mini
mental state examination, g-
factor general cognitive factor
Table 4 Association of age with global cognitive function
N=3,021 Total Men Women P
interaction
*P
quadratic
**
MMSE -0.24 (-0.30; -0.18) -0.30 (-0.39; -0.22) -0.19 (-0.28; -0.10) 0.11 \0.01
G-factor -0.59 (-0.62; -0.56) -0.60 (-0.64; -0.56) -0.58 (-0.62; -0.54) 0.73 \0.01
Values represent differences in MMSE score and g-factor per 10 year increase, adjusted for level of education
MMSE mini mental state examination, g-factor general cognitive factor
*Pvalue for interaction between age and sex
** Pvalue for quadratic effect of age on cognition for total sample
Stroop reading
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Stroop color naming
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Stroop interference
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Letter-Digit Substitution Task
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Verbal fluency
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
15-WLT immediate recall
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
15-WLT delayed recall
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
15-WLT recognition
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Design Organization Test
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Purdue Pegboard Test
Age per 5 years
45 - 49
50 - 54
55 - 59
60 - 64
65 - 69
70 - 74
75 - 79
80 - 84
85 - 89
90+
Z-score
-1.5
-1.0
-0.5
0.0
0.5
1.0
Fig. 2 Cognitive function in 5-years bins. The x-axis represents age per 5 years and the y-axis represents the z-score on the test. Error bars
represent 95 % confidence intervals. All estimates are adjusted for level of education. 15-WLT 15-Word learning test
The Rotterdam Study 137
123

Strengths of this study include the large community-
dwelling study sample and availability of multiple cogni-
tive tests. An important limitation to the interpretation of
our results is the cross-sectional design. Also, relations
between age and cognition could partly be influenced by
cohort effects. However, differences in age effects across
cognitive tests are comparable since all analyses were
performed on the same group of persons. Another problem
is that not all cognitive tests were completed by all par-
ticipants to our study and that participants are younger and
usually in better health compared to non-participants [24].
Therefore, in our g-factor analyses, we selected a sample
with fully available cognitive data. We should keep in
mind that this may have introduced some selection bias and
has may reduce the generalizability of the results. We also
note that in order to summarize the different cognitive tests
into one g-factor, we selected six cognitive test variables
under the assumption that these are representatives of
various cognitive domains (executive function, processing
speed, verbal fluency, memory, visuospatial ability, and
fine motor skill), which are frequently used in cognitive
aging research. Other studies may select different tests to
construct a g-factor and will possibly get a slightly dif-
ferent outcome. However, it was previously found that
g-factors constructed from variable test batteries result in
factors that are highly correlated [13]. Thus, the g-factor is
likely to be a stable concept. It is comprised of shared
variance between tests, and can be interpreted as a factor
which is common to a variety of cognitive domains.
In this study sample, we showed that the g-factor is
affected already from age 45 onwards. Also, compared to
the other cognitive tests in our battery, the g-factor was
most strongly related to age. The strength of relation
between age and cognition was consistent with those found
by others [25,26]. MMSE score only showed a decline
from age 70 onwards. The MMSE is often used to test
global cognitive function in older adults, yet it has fre-
quently been criticised for its ceiling effect [27,28]. In
agreement with a large study of healthy elderly, we did not
find strong effects of age on MMSE score [29].
Among our other cognitive tests, we found that fine
motor skill, processing speed and visuospatial ability were
most affected by age. In agreement with the observed
relation between age and visuospatial ability, WAIS-III
block design performance starts to decline from the mid-
forties onward [12]. Other studies have also suggested a
more prominent role for decline in visuospatial ability in
aging research [30,31]. One study reported a composite
score of visuospatial ability to be a significant predictor of
developing cognitive decline [2]. However, another large
cohort study reported relatively small effects of age on
visuospatial ability [4]. Already in the youngest age groups
we found an effect of age on performance on the Purdue
pegboard test. Population studies in the healthy elderly that
looked into age effects on fine motor skills are scarce. The
relatively large age effects on the LDST are in line with
previous studies showing strong age effects on processing
speed [9,32]. Interestingly, these findings are supported by
indirect evidence from neuroimaging studies which found
that white matter declined faster than grey matter and white
matter deterioration was associated with decline in motor
skill and tasks of processing speed [33–35]. However,
others concluded there is a relative stability of white matter
volume in aging [36,37]. The effect size we found relating
Table 5 Association of age with cognitive test scores
Total Men Women P
interaction
*P
quadratic
**
Stroop reading, n =4,042 -0.32 (-0.35; -0.29) -0.31 (-0.35; -0.26) -0.31 (-0.35; -0.27) 0.43 \0.01
Stroop naming, n =4,041 -0.32 (-0.35; -0.29) -0.34 (0.39; -0.29) -0.30 (-0.33; -0.26) 0.10 \0.01
Stroop interference, n =4,030 -0.41 (-0.44; -0.38) -0.43 (-0.47; -0.38) -0.40 (-0.44; -0.36) 0.12 \0.01
Letter-digit substitution task, n =4,074 -0.49 (-0.51; -0.46) -0.42 (-0.46; -0.38) -0.53 (-0.56; -0.49) 0.01 \0.01
Verbal fluency test, n =4,176 -0.32 (-0.35; -0.29) -0.29 (-0.33; -0.24) -0.34 (-0.38; -0.30) 0.19 \0.01
15-Word learning test immediate recall,
n=3,826
-0.25 (-0.28; -0.22) -0.25 (-0.30; -0.21) -0.25 (-0.29; -0.21) 0.77 0.25
15-Word learning test delayed recall,
n=3,825
-0.23 (-0.26; -0.20) -0.23 (-0.28; -0.18) -0.23 (-0.28; -0.19) 0.75 0.71
15-Word learning test recognition,
n=3,902
-0.09 (-0.12; -0.05) -0.09 (-0.15; -0.04) -0.08 (-0.13; -0.04) 0.75 0.39
Design organization test, n =3,706 -0.48 (-0.51; -0.45) -0.50 (-0.55; -0.46) -0.46 (-0.50; -0.42) 0.21 0.12
Purdue pegboard test, n =3,801 -0.53 (-0.56; -0.50) -0.48 (-0.53; -0.44) -0.56 (-0.60; -0.53) 0.02 \0.01
Values represent differences in cognitive test scores per 10-year increase, adjusted for level of education. All cognitive scores are expressed as
z-scores
*Pvalue for interaction between age and sex
** Pvalue for quadratic effect of age on cognition for total sample
138 Y. Y. Hoogendam et al.
123

age to memory was small compared to age effects on other
cognitive scores. Again, this is in line with evidence
showing that memory function is more dependent on grey
matter which decreases gradually with aging [38,39].
Furthermore, we found that women scored better on
memory tests than men, which is in accordance with pre-
vious findings that women have better verbal memory than
men [40,41]. No difference in age effects on memory was
found between men and women. It is expected that mem-
ory would be more strongly affected in dementia rather
than normal aging. The exclusion of prevalent dementia
cases from our study possibly contributed to the small
negative effects of age on memory. However, there is a
continuum between normal cognitive aging and dementia,
and persons in the preclinical stages of dementia were not
excluded from the study population. Normal cognitive
aging research has often found that the more frontal brain
functions such as attention and executive function are
affected earlier than memory [42–44]. The relatively
smaller effect on the verbal fluency test may reflect the fact
that we used a category fluency test rather than a phonemic
fluency test. Category fluency places a larger demand on
memory performance rather than frontal lobe function [45,
46]. Furthermore, we found a stronger effect on the color-
word interference subtask of the Stroop, compared to the
reading and naming subtasks. The Stroop color-word
interference task requires more cognitive control than the
first two subtasks and is more dependent on executive
function, specifically on attention and inhibition [19].
In conclusion, in persons of 45 years and older, age is
most strongly related general cognitive function. Our
findings also suggest that not memory, but fine motor skill,
processing speed, and visuospatial ability are affected most
by advancing age.
Acknowledgments JN van der Geest was supported by the Princes
Beatrix Fonds. The Rotterdam Study is sponsored by the Erasmus
Medical Center and Erasmus University Rotterdam, The Netherlands
Organization for Scientific Research (NWO), The Netherlands
Organization for Health Research and Development (ZonMW), the
Research Institute for Diseases in the Elderly (RIDE), The Nether-
lands Genomics Initiative, the Ministry of Education, Culture and
Science, the Ministry of Health, Welfare and Sports, the European
Commission (DG XII), and the Municipality of Rotterdam.
Conflict of interest The authors declare that they have no conflict
of interest.
References
1. Albert MS. Changes in cognition. Neurobiol Aging. 2011;32(Suppl
1):S58–63.
2. Johnson DK, Storandt M, Morris JC, Galvin JE. Longitudinal
study of the transition from healthy aging to Alzheimer disease.
Arch Neurol. 2009;66(10):1254–9.
3. Hedden T, Oh H, Younger AP, Patel TA. Meta-analysis of
amyloid-cognition relations in cognitively normal older adults.
Neurology. 2013;80(14):1341–8.
4. Bennett DA, Wilson RS, Schneider JA, Evans DA, Beckett LA,
Aggarwal NT, et al. Natural history of mild cognitive impairment
in older persons. Neurology. 2002;59(2):198–205.
5. Tucker-Drob EM. Global and domain-specific changes in cog-
nition throughout adulthood. Dev Psychol. 2011;47(2):331–43.
6. Ylikoski R, Ylikoski A, Keskivaara P, Tilvis R, Sulkava R, Er-
kinjuntti T. Heterogeneity of cognitive profiles in aging: suc-
cessful aging, normal aging, and individuals at risk for cognitive
decline. Eur J Neurol. 1999;6(6):645–52.
7. Finkel D, Reynolds CA, McArdle JJ, Pedersen NL. Cohort dif-
ferences in trajectories of cognitive aging. J Gerontol Ser B
Psychol Sci Soc Sci. 2007;62(5):P286–94.
8. Gerstorf D, Ram N, Hoppmann C, Willis SL, Schaie KW. Cohort
differences in cognitive aging and terminal decline in the Seattle
Longitudinal study. Dev Psychol. 2011;47(4):1026–41.
9. Finkel D, Reynolds CA, McArdle JJ, Pedersen NL. Age changes
in processing speed as a leading indicator of cognitive aging.
Psychol Aging. 2007;22(3):558–68.
10. Deary IJ, Johnson W, Starr JM. Are processing speed tasks bio-
markers of cognitive aging? Psychol Aging. 2010;25(1):219–28.
11. Johnson DK, Storandt M, Morris JC, Langford ZD, Galvin JE.
Cognitive profiles in dementia: Alzheimer disease vs healthy
brain aging. Neurology. 2008;71:1783–9.
12. Deary IJ. Intelligence. Annu Rev Psychol. 2012;63:453–82.
13. Johnson W, te Nijenhuis J, Bouchard TJ Jr. Still just 1 g: con-
sistent results from five test batteries. Intelligence. 2008;36(1):
81–95.
14. Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HL,
Klaver CC, et al. The Rotterdam Study: 2012 objectives and
design update. Eur J Epidemiol. 2011;26(8):657–86.
15. Schrijvers EM, Verhaaren BF, Koudstaal PJ, Hofman A, Ikram
MA, Breteler MM. Is dementia incidence declining? Trends in
dementia incidence since 1990 in the Rotterdam Study. Neurol-
ogy. 2012;78(19):1456–63.
16. Wieberdink RG, Ikram MA, Hofman A, Koudstaal PJ, Breteler
MM. Trends in stroke incidence rates and stroke risk factors in
Rotterdam, the Netherlands from 1990 to 2008. Eur J Epidemiol.
2012;27(4):287–95.
17. Folstein MF, Folstein SE, McHugh PR. ‘‘Mini-mental state’’: a
practical method for grading the cognitive state of patients for the
clinician. J Psychiatr Res. 1975;12(3):189–98.
18. Houx PJ, Jolles J, Vreeling FW. Stroop interference: aging effects
assessed with the Stroop color-word test. Exp Aging Res.
1993;19(3):209–24.
19. Lezak MD, Howieson DB, Loring DW. Neuropsychological
assessment. New York: Oxford University Press; 2004.
20. Welsh KA, Butters N, Mohs RC, Beekly D, Edland S, Fillenbaum
G, et al. The consortium to establish a registry for Alzheimer’s
disease (CERAD). Part V. A normative study of the neuropsy-
chological battery. Neurology. 1994;44(4):609–14.
21. Bleecker ML, Bolla-Wilson K, Agnew J, Meyers DA. Age-
related sex differences in verbal memory. J Clin Psychol.
1988;44(3):403–11.
22. Killgore W, Glahn D, Casasanto D. Development and validation
of the design organization test (DOT): a rapid screening instru-
ment for assessing visuospatial ability. J Clin Exp Neuropsychol.
2005;27:449–59.
23. Tiffin J, Asher EJ. The Purdue pegboard; norms and studies of
reliability and validity. J Appl Psychol. 1948;32(3):234–47.
24. van Rossum CT, van de Mheen H, Witteman JC, Hofman A,
Mackenbach JP, Grobbee DE. Prevalence, treatment, and control
of hypertension by sociodemographic factors among the Dutch
elderly. Hypertension. 2000;35(3):814–21.
The Rotterdam Study 139
123

25. Wilson RS, Beckett LA, Bennett DA, Albert MS, Evans DA.
Change in cognitive function in older persons from a community
population: relation to age and Alzheimer disease. Arch Neurol.
1999;56(10):1274–9.
26. Hayden KM, Reed BR, Manly JJ, Tommet D, Pietrzak RH,
Chelune GJ, et al. Cognitive decline in the elderly: an analysis of
population heterogeneity. Age Ageing. 2011;40(6):684–9.
27. Mungas D, Reed BR. Application of item response theory for
development of a global functioning measure of dementia with
linear measurement properties. Stat Med. 2000;19(11–12):1631–44.
28. Glymour MM, Tzourio C, Dufouil C. Is cognitive aging predicted
by one’s own or one’s parents’ educational level? Results from
the three-city study. Am J Epidemiol. 2012;175(8):750–9.
29. Starr JM, Deary IJ, Inch S, Cross S, MacLennan WJ. Age-asso-
ciated cognitive decline in healthy old people. Age Ageing.
1997;26(4):295–300.
30. Jenkins L, Myerson J, Joerding JA, Hale S. Converging evidence
that visuospatial cognition is more age-sensitive than verbal
cognition. Psychol Aging. 2000;15:157–75.
31. Klencklen G, Despres O, Dufour A. What do we know about
aging and spatial cognition? Reviews and perspectives. Ageing
Res Rev. 2012;11(1):123–35.
32. Salthouse TA. The processing-speed theory of adult age differ-
ences in cognition. Psychol Rev. 1996;103(3):403–28.
33. Sullivan EV, Rohlfing T, Pfefferbaum A. Quantitative fiber
tracking of lateral and interhemispheric white matter systems in
normal aging: relations to timed performance. Neurobiol Aging.
2010;31:464–81.
34. Sachdev PS, Wen W, Christensen H, Jorm AF. White matter
hyperintensities are related to physical disability and poor motor
function. J Neurol Neurosurg Psychiatr. 2005;76(3):362–7.
35. Vernooij MW, Ikram MA, Vrooman HA, Wielopolski PA,
Krestin GP, Hofman A, et al. White matter microstructural
integrity and cognitive function in a general elderly population.
Arc Gen Psychiatr. 2009;66:545–53.
36. Deary IJ, Corley J, Gow AJ, Harris SE, Houlihan LM, Marioni
RE, et al. Age-associated cognitive decline. Br Med Bull.
2009;92:135–52.
37. Sullivan EV, Pfefferbaum A. Diffusion tensor imaging and aging.
Neurosci Biobehav Rev. 2006;30(6):749–61.
38. Ikram MA, Vrooman HA, Vernooij MW, den Heijer T, Hofman A,
Niessen WJ, et al. Brain tissue volumes in relation to cognitive
functionand risk of dementia. Neurobiol Aging. 2010;31(3):378–86.
39. Ikram MA, Vrooman HA, Vernooij MW, van der Lijn F, Hofman
A, van der Lugt A, et al. Brain tissue volumes in the general
elderly population. The Rotterdam Scan Study. Neurobiol Aging.
2008;29(6):882–90.
40. Herlitz A, Nilsson LG, Backman L. Gender differences in epi-
sodic memory. Mem Cognit. 1997;25(6):801–11.
41. Herlitz A, Yonker JE. Sex differences in episodic memory: the influ-
ence of intelligence. J Clin Exp Neuropsychol. 2002;24(1):107–14.
42. Buckner RL. Memory and executive function in aging and AD:
multiple factors that cause decline and reserve factors that com-
pensate. Neuron. 2004;44(1):195–208.
43. Raz N, Rodrigue KM. Differential aging of the brain: patterns,
cognitive correlates and modifiers. Neurosci Biobehav Rev.
2006;30(6):730–48.
44. DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, et al.
Measures of brain morphology and infarction in the framingham
heart study: establishing what is normal. Neurobiol Aging.
2005;26:491–510.
45. Baldo JV, Schwartz S, Wilkins D, Dronkers NF. Role of frontal
versus temporal cortex in verbal fluency as revealed by voxel-
based lesion symptom mapping. J Int Neuropsychol Soc.
2006;12(6):896–900.
46. Schwartz S, Baldo J, Graves RE, Brugger P. Pervasive influence
of semantics in letter and category fluency: a multidimensional
approach. Brain Lang. 2003;87(3):400–11.
140 Y. Y. Hoogendam et al.
123