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Original Article
Low-grade carotid artery stenosis is
associated with progression of brain
atrophy and cognitive decline.
The SMART-MR study
Rashid Ghaznawi
1,2
, Jet MJ Vonk
2
, Maarten HT Zwartbol
1
,
Jeroen de Bresser
3
, Ina Rissanen
2
, Jeroen Hendrikse
1
and
Mirjam I Geerlings
2
;
on behalf of the UCC-SMART Study Group
4
Abstract
Asymptomatic low-grade carotid artery stenosis (LGCS) is a common finding in patients with manifest arterial disease,
however its relationship with brain MRI changes and cognitive decline is unclear. We included 902 patients (58 10
years; 81% male) enrolled in the Second Manifestations of Arterial Disease – Magnetic Resonance (SMART-MR) study
without a history of cerebrovascular disease. LGCS was defined as 1–49% stenosis on baseline carotid ultrasound,
whereas no LGCS (reference category) was defined as absence of carotid plaque. Brain and white matter hyperintensity
(WMH) volumes and cognitive function were measured at baseline and after 4 (n ¼480) and 12 years (n ¼222) of
follow-up. Using linear mixed-effects models, we investigated associations of LGCS with progression of brain atrophy,
WMH, and cognitive decline. LGCS was associated with greater progression of global brain atrophy (estimate 0.03;
95%CI, 0.06 to 0.01; p¼0.002), and a greater decline in executive functioning (estimate 0.02; 95%CI, 0.031 to
0.01; p<0.001) and memory (estimate 0.012; 95%CI, 0.02 to 0.001; p¼0.032), independent of demographics,
cardiovascular risk factors, and incident brain infarcts on MRI. No association was observed between LGCS and pro-
gression of WMH. Our results indicate that LGCS may represent an early marker of greater future brain atrophy and
cognitive decline.
Keywords
Brain atrophy, cognitive decline, cohort studies, low-grade carotid artery stenosis, white matter hyperintensity
Received 17 May 2022; Revised 14 August 2022; Accepted 5 September 2022
Introduction
Carotid artery stenosis refers to the buildup of athero-
sclerotic plaque along the lining of the carotid arteries
and represents a well-recognized cause of atheroem-
bolic stroke.
1
At the highest levels of stenosis, carotid
atheroma may also lead to hemodynamic stroke
through flow restriction and cerebral ischemic injury.
2
Mild carotid atheroma resulting in low-grade
(1–49%) carotid artery stenosis (LGCS) is associated
with a relatively low risk of atheroembolic stroke com-
pared to moderate or severe stenosis,
3,4
but is a
common finding in older individuals and patients
with atherosclerotic disease.
5–8
In clinical practice,
1
Department of Radiology, University Medical Center Utrecht and
Utrecht University, Utrecht, the Netherlands
2
Julius Center for Health Sciences and Primary Care, University Medical
Center Utrecht and Utrecht University, Utrecht, the Netherlands
3
Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands
4
Listed in the Acknowledgements
Corresponding author:
Mirjam I Geerlings, Julius Center for Health Sciences and Primary Care,
University Medical Center Utrecht, P.O. Box 85500, Stratenum 6.131,
3508 GA Utrecht, The Netherlands.
Email: m.geerlings@umcutrecht.nl
Journal of Cerebral Blood Flow &
Metabolism
2023, Vol. 43(2) 309–318
!The Author(s) 2022
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0271678X221133859
journals.sagepub.com/home/jcbfm
LGCS is frequently identified incidentally in asymp-
tomatic patients on imaging studies.
9
Results from
recent cross-sectional studies suggest that asymptomat-
ic LGCS may be of clinical importance as a risk factor
for smaller brain volumes and worse cognitive perfor-
mance.
10
The cross-sectional design of these studies,
however, precludes establishing a cause-effect relation-
ship. To the best of our knowledge, no previous studies
examined the longitudinal relationship of LGCS with
brain MRI changes and cognitive decline in patients
without a history of cerebrovascular disease.
Here, we tested the hypothesis that asymptomatic
LGCS may represent a risk factor for greater progres-
sion of brain atrophy, WMH and cognitive decline.
Using data from the Second Manifestations of
ARTerial disease-Magnetic Resonance (SMART-
MR) study, we compared trajectories of brain volumes,
WMH volumes, and cognitive domains between
patients with asymptomatic LGCS and patients with-
out LGCS over 12 years of follow-up, adjusting for
demographics and cardiovascular risk factors.
Methods
Study population
We used data from the SMART-MR study, a prospec-
tive cohort study at the University Medical Center
Utrecht to investigate risk factors and consequences
of brain changes on MRI in patients with manifest
arterial disease.
11,12
A total of 1,309 adult patients
newly referred to the University Medical Center
Utrecht for treatment of atherosclerotic disease (man-
ifest coronary artery disease, cerebrovascular disease,
peripheral arterial disease or abdominal aortic aneu-
rysm) between 2001 and 2005 were included for base-
line measurements, including a 1.5 T brain MRI.
11,12
During a 1-day visit to the University Medical Center
Utrecht, a physical examination, ultrasonography
of the carotid arteries, blood and urine samplings,
and a 1.5 T brain MRI scan were performed.
11,12
Neuropsychological assessment was added to the
research protocol from 2003 onwards. We used ques-
tionnaires to assess demographics, risk factors, medical
history, medication use, and cognitive and physical
functioning.
11,12
Of the 1,309 patients included, 754
patients had follow-up measurements four years later
between January 2006 and May 2009. Between
November 2013 and October 2017, all patients alive
were invited for a second follow-up, including a 1.5 T
brain MRI. Second follow-up measurements were
obtained from 329 patients.
The SMART-MR study was approved by the med-
ical ethics committee of the University Medical Center
Utrecht according to the guidelines of the Declaration
of Helsinki of 1975. Written informed consent was
obtained from all patients participating in the
SMART-MR study.
Study sample
Of the 1,309 patients included in the SMART-MR
study, carotid ultrasound data were irretrievable or
incomplete in 56 patients and 181 patients were cate-
gorized as having moderate (50-69%) or severe (>70%)
carotid stenosis. These patients were excluded from the
present analyses. In addition, we excluded 170 patients
with a history of cerebrovascular disease (defined as
transient ischemic attack, stroke, cerebral ischemia,
amaurosis fugax, or retinal infarction)
13
as these may
include patients with a symptomatic LGCS, resulting in
a study sample of 902 patients (LGCS n ¼713; no
LGCS n ¼189). Flow diagrams of patients with avail-
able neuroimaging and cognition data at each visit are
shown in Figure 1 and Figure 2 of the Supplemental
Material, respectively.
Carotid stenosis
At baseline, ultrasonography consisting of color
Doppler-assisted duplex scanning was performed with
a 10 MHz linear-array transducer (ATL Ultramark 9)
by experienced ultrasound technicians to determine the
presence and degree of carotid stenosis. The severity of
carotid stenosis was evaluated based on blood flow
velocity patterns and presence of plaque, and was
recorded on a categorical scale.
14,15
The greatest stenosis
observed on the right or the left side of the common or
internal carotid artery was taken to determine the sever-
ity of carotid artery disease. Carotid stenosis 1–29% and
Figure 1. Longitudinal relationship between executive func-
tioning (z-score), low-grade carotid stenosis, and no stenosis.
Age of patients at each visit was chosen as the time variable. The
shaded grey area represents the 95% confidence interval. Results
adjusted for sex, education level, large infarcts on MRI, lacunes
on MRI, hypertension, diabetes mellitus, body mass index,
smoking pack years, alcohol use and practice effect.
310 Journal of Cerebral Blood Flow & Metabolism 43(2)
30–49% were defined as presence of plaque with a peak
systolic velocity (PSV) 100 cm/s and >100 to
150 cm/s, respectively. In the present study, we defined
LGCS as 1–49% stenosis. No LGCS (reference catego-
ry) was defined as absence of carotid plaque.
MRI protocol
MR imaging of the brain was performed on a 1.5 T
whole-body system (Gyroscan ACS-NT, Philips
Medical Systems, Best, the Netherlands) using a stan-
dardized scan protocol.
11
Transversal T1-weighted
[repetition time (TR) ¼235 ms; echo time (TE) ¼
2 ms], T2-weighted [TR ¼2200 ms; TE ¼11 ms], fluid-
attenuated inversion recovery (FLAIR) [TR ¼6000 m;
TE ¼100 ms; inversion time (TI) ¼2000 ms] and T1-
weighted inversion recovery images [TR ¼2900 ms;
TE ¼22 ms; TI ¼410 ms] were acquired with a voxel
size of 1.0 1.0 4.0 mm
3
and contiguous slices.
12
Brain infarcts
Although we excluded patients with a history of symp-
tomatic cerebrovascular disease, patients in the study
sample may show clinically non-manifest brain infarcts
on MRI (i.e., silent cerebrovascular disease), which
may confound the relationship between LGCS and
change in neuroimaging and cognitive outcomes. We
therefore accounted for brain infarcts on MRI in our
analyses. Brain infarcts were visually rated by a neuro-
radiologist blinded to patient characteristics on the
T1-weighted, T2-weighted, and FLAIR images of the
MRI scans. Lacunes were defined as focal lesions
between 3 to 15 mm according to the STRIVE crite-
ria,
16
whereas non-lacunar lesions were categorized in
large infarcts (i.e., cortical infarcts and subcortical
infarcts not involving the cerebral cortex) and infarcts
located in the cerebellum or brain stem.
Brain volume measurements
White matter hyperintensity (WMH) volumes and
brain volumes were obtained using the k-nearest neigh-
bor (kNN) automated segmentation program on the
T1-weighted, FLAIR, and T1-weighted inversion
recovery sequences of the MRI scans.
17
The kNN seg-
mentation method has been shown to be suitable for
detecting longitudinal brain volume changes.
11,18
All
WMH segmentations were visually checked by an
investigator (RG) blinded to patient characteristics
using an image processing framework (MeVisLab
2.7.1., MeVis Medical Solutions AG, Bremen,
Germany). Incorrectly segmented voxels were added
to the correct segmentation volumes using the image
processing framework.
Total brain volume (including the volume of the cer-
ebellum) was calculated by summing the volumes of
gray matter, white matter, WMH and, if present, the
volumes of brain infarcts. Total intracranial volume
(ICV) was calculated by summing the total brain
volume and the volume of cerebrospinal fluid. Total
brain volume, sulcal cerebrospinal fluid volume and
ventricular volume were normalized for ICV and
expressed as brain parenchymal fraction (BPF), sulcal
cerebrospinal fluid fraction (CSFF) and ventricular
fraction (VF), and were used as indicators of global,
cortical, and subcortical atrophy, respectively.
Similarly, WMH volume was normalized for ICV.
Natural log transformation was performed on WMH
volumes due to a non-normal distribution.
Cognitive functioning
Cognitive functioning was measured at baseline, and
first and second follow-up visits with a set of standard
neuropsychological tests covering the domains of
memory and executive functioning. Memory was
assessed with the 15 Word Learning test (immediate
recall based on five trials and delayed recall) and with
the delayed recall of the Rey-Osterrieth Complex
Figure test.
19,20
Executive functioning was assessed by
the Visual Elevator test (10 trials), the Brixton Spatial
Anticipation test, and the Verbal Fluency test (letter A
with a time span of 60 seconds).
21–23
Visual Elevator
test scores were natural log-transformed due to a non-
normal distribution and multiplied by minus one so
that higher scores represented better performance.
Similarly, Brixton test scores were multiplied by
minus one so that higher scores represented better
performance.
Figure 2. Longitudinal relationship between memory (z-score),
low-grade carotid stenosis, and no stenosis. Age of patients at
each visit was chosen as the time variable. The shaded grey area
represents the 95% confidence interval. Results adjusted for sex,
education level, large infarcts on MRI, lacunes on MRI, hyper-
tension, diabetes mellitus, body mass index, smoking pack years,
alcohol use and practice effect.
Ghaznawi et al. 311
To assess change in cognitive functioning, we con-
verted test scores from each visit to z-scores based on
the baseline population mean and standard deviation
(SD). These z-scores were averaged to create domain-
specific z-scores for memory and executive functioning,
which were subsequently standardized to the baseline
domain-specific z-score mean and SD for all patients.
Covariates
At baseline, age, sex, smoking habits, alcohol intake
and highest level of education were assessed using ques-
tionnaires. Height and weight were measured, and the
body mass index (BMI) was calculated (kg/m
2
).
Systolic blood pressure (SBP) (mmHg) and diastolic
blood pressure (DBP) (mmHg) were measured three
times with a sphygmomanometer, and the average of
these measures was calculated. Hypertension was
defined as a mean SBP of >160 mmHg, a mean DBP
of >95 mmHg, or self-reported use of antihypertensive
drugs. Threshold values of SBP and DBP for hyperten-
sion were determined according to criteria established
in 2001. An overnight fasting venous blood sample was
taken to determine glucose and lipids. Diabetes melli-
tus was defined as fasting serum glucose levels of
7.0 mmol/l, and/or use of glucose-lowering medica-
tion, and/or known history of diabetes.
Education level was categorized into three categories
based on the Dutch education system and ranged from
no education/primary school to university education.
Low level education included no education or primary
school only (comparable to up to six years of
education), whereas high level education included
higher professional education and university education
(comparable to 15 years of education). All other edu-
cational levels were defined as intermediate (compara-
ble to around 7–14 years of education).
Statistical analysis
Baseline characteristics of the total study sample, and
stratified by presence and absence of LGCS were
reported with descriptive statistics. We compared base-
line characteristics of patients with LGCS versus those
without using an independent samples t-test and Chi
square test for continuous and dichotomous variables,
respectively.
Linear mixed-effects models. We used linear mixed-effects
models with random effects to assess changes in
neuroimaging outcomes and cognitive functioning
over time.
24
The age of patients at each visit was
chosen as the time variable, which was centered at 58
years (the mean value at the first visit) and hereinafter
referred to as ‘time’. LGCS was represented by a
dichotomous variable with absence of LGCS as the
reference category.
Models were run in two steps. In the first model,
time, LGCS, and an interaction term between LGCS
and time (our primary coefficient of interest) were
entered, together with sex, large infarcts on MRI,
lacunes on MRI, hypertension, diabetes mellitus,
body mass index, smoking pack years and alcohol use
at baseline as covariates. Models that estimated cogni-
tive change in addition included education level and a
practice effect, which was modeled using an indicator
fixed at the square root of the number of prior visits.
25
Incident brain infarcts and lacunes may act as a
confounder on the relationship between LGCS and
change in neuroimaging and cognitive outcomes.
Therefore, in a second model, the covariates indicating
large infarcts and lacunes on baseline MRI were
replaced with time-varying covariates indicating the
presence of large infarcts and lacunes at both baseline
and follow-up MRI.
To determine whether brain atrophy was associated
with cognitive functioning at baseline and follow-up,
we also added BPF as a time-varying predictor to the
models that estimated cognitive change.
Adequacy of the linear mixed-effects models was
determined by examining the residuals for approximate
normality and homoscedasticity. We concluded that
model assumptions were adequately met.
Missing covariates. To reduce the risk of bias due to com-
plete case analysis, we performed chained equations
imputation on missing baseline covariates to generate
10 imputed datasets using SPSS 25.0 (Chicago, IL,
USA). The linear mixed-effects models were performed
on the imputed datasets and the pooled results were
presented. Statistical significance was set at p0.05.
Sensitivity analysis. The substantial attrition during
follow-up in the present study may lead to informative
dropout. To determine whether this was the case in the
study sample, we used joint models that allow for con-
trolling the results of the linear mixed models for drop-
out (including due to death) using correlated survival
data.
26
Joint models consist of a longitudinal and a
survival submodel.
26
The longitudinal submodel con-
sisted of the linear mixed-effects models used in the
primary analyses with adjustment for demographics,
cardiovascular risk factors and brain infarcts on MRI
at baseline. The survival submodel consisted of a Cox
proportional hazards regression model with baseline
age, sex and LGCS (with absence of LGCS as the ref-
erence category) as predictors. Follow-up data for the
survival submodel were obtained from questionnaires
that patients received biannually and are described in
detail in previous work.
12
We defined dropout (i.e., the
312 Journal of Cerebral Blood Flow & Metabolism 43(2)
“event” in the survival submodel) as having a missing
outcome for the second follow-up measurement, either
due to death or any other reason.
We compared joint models using different baseline
hazard functions and we selected the baseline hazard
function that yielded the lowest Akaike information
criterion. The Weibull baseline hazard function was
chosen for models that estimated change in brain vol-
umes, whereas the piecewise baseline hazard function
was chosen for models that estimated change in cogni-
tive functioning. The JM package for R version 4.0.5
(R Core Team, 2021) was used for the joint model
analysis.
26
Results
Baseline characteristics of the study sample (n ¼902;
mean age 58 10 years; 81% male) are shown in
Table 1. LGCS was present in 713 patients (79%) at
baseline, whereas 189 patients (21%) did not show any
carotid stenosis on ultrasound.
Patients with LGCS were older, had a less favorable
cardiovascular profile, more often had a low education
level, and showed smaller brain volumes and lower
executive functioning and memory z-scores compared
to the reference group (Table 1).
Mean time between baseline and first follow-up
measurements was 3.9 0.4 years (range 2.9–5.8
years), whereas there were 12.0 0.4 years (range
11.1–13.5 years) between baseline and the second
follow-up measurements.
Associations between LGCS and brain MRI changes
Mean decrease in BPF per year for the study sample
was 0.25% ICV (95% CI, –0.28 to –0.22), whereas
CSFF and VF were estimated to increase at 0.19%
ICV (95% CI, 0.16 to 0.22) and 0.06% ICV (95%
CI, 0.05 to 0.06) per year, respectively (Table 2).
Table 1. Baseline characteristics of patients with low-grade carotid stenosis, patients without stenosis and the total study sample.
Low-grade carotid
stenosis (n ¼713)
No carotid
stenosis (n ¼189)
All patients
(n ¼902) p-value
a
Age (years) 59 95110 58 10 <0.001
Sex, % men 81.5 78.3 80.8 0.09
BMI (kg/m
2
)274264274 0.08
Smoking, pack years
b
20 (0, 52) 14 (0, 42) 19 (0, 49) <0.001
c
Alcohol use, %
Current 75 74 75 0.52
Former 10 9 10 0.36
Abstinent 15 17 15 0.12
Hypertension, % 48.8 37.6 46.5 <0.001
Diabetes mellitus, % 20.9 10.1 18.6 <0.001
Education level, %
Low 13.0 8.4 11.9 0.005
Intermediate 67.5 63.5 66.6 0.08
High 19.5 28.1 21.5 <0.001
Infarcts on MRI, %
Large 3.5 1.1 3.0 0.002
Cerebellar 2.7 2.7 2.5 0.47
Brainstem 1.4 0.5 1.2 0.09
Lacunes on MRI, % 10.9 3.2 9.3 <0.001
pCBF, ml/min per 100 ml brain volume 52.2 10.3 53.2 9.0 52.4 10.1 0.03
BPF, % ICV 79.0 2.8 80.7 2.5 79.3 2.8 <0.001
CSFF, % ICV 18.9 2.3 17.5 2.0 18.6 2.3 <0.001
VF, % ICV 2.1 1.1 1.8 1.0 2.0 1.0 <0.001
WMH volume on MRI, ml
b
0.9 (0.2, 5.6) 0.5 (0.1, 2.2) 0.8 (0.2, 4.9) <0.001
c
Executive functioning, z-score 0.02 0.96 0.23 0.98 0.05 0.97 <0.001
Memory, z-score 0.10 0.97 0.27 0.96 0.00 0.98 <0.001
Characteristics are presented as mean SD or %.
a
P-value for independent samples t-test or Chi square test (if proportions) for comparison between patients with low-grade carotid stenosis and
patients without carotid stenosis.
b
Median (10th percentile, 90th percentile).
c
Natural log-transformed due to a non-normal distribution in the statistical analysis.
BMI: body mass index; SD: standard deviation; WMH: white matter hyperintensity; pCBF: parenchymal cerebral blood flow; BPF: brain parenchymal
fraction; ICV: total intracranial volume; CSFF: sulcal cerebrospinal fluid fraction; VF: ventricular fraction.
Ghaznawi et al. 313
Mean increase in WMH per year was 0.08 natural log-
transformed ml (95% CI, 0.07 to 0.10).
At age 58 (i.e., intercept), no main effects were
observed of LGCS on BPF (estimate 0.07; 95% CI,
–0.30 to 0.40; p¼0.717), CSFF (estimate 0.07; 95%
CI, –0.23 to 0.37; p¼0.661), VF (estimate –0.10; 95%
CI, –0.24 to 0.03; p¼0.134), or WMH volume (esti-
mate 0.00; 95% CI, –0.18 to 0.17; p¼0.978).
Significant main effects of sex, lacunes on MRI, diabe-
tes mellitus, and smoking pack years were observed on
BPF, CSFF, VF and WMH volume (Supplementary
Table 1).
LGCS, compared with no LGCS, was associated
with greater change in BPF (estimate –0.03; 95% CI,
–0.06 to –0.01; p¼0.002), CSFF (estimate 0.03; 95%
CI, 0.01 to 0.05; p¼0.011) and VF (estimate 0.01; 95%
CI, 0.002 to 0.02; p¼0.019), and these results did not
substantially change after adjusting for incident large
brain infarcts or lacunes (Table 2). LGCS was not asso-
ciated with greater change in WMH volume over time
(estimate 0.01; 95% CI, –0.03 to 0.02; p¼0.162).
In the joint model analysis, parameter estimates for
the time effect were slightly smaller for BPF, CSFF, VF
and WMH compared with the primary analysis
(Supplementary Table 3). Controlling for death/dropout,
LGCS versus no LGCS remained significantly associ-
ated with a greater decline in BPF (estimate –0.036;
95% CI, –0.06 to –0.01; p¼0.001), and a greater
increase in CSFF (estimate 0.03; 95% CI, 0.01 to
0.05; p¼0.006) and VF (estimate 0.01; 95% CI, 0.002
to 0.017; p¼0.013). Consistent with the primary anal-
ysis, LGCS was not related to a greater change in
WMH volume (estimate 0.007; 95% CI, –0.003 to
0.017; p¼0.179). Estimates of association parameters
were significant for CSFF (estimate 0.0485; p¼0.036)
and VF (estimate 0.143; p¼0.002), thereby indicating
that death/dropout impacted average change in
CSFF and VF over time, whereas this was not the
case for BPF (estimate –0.037; p¼0.063) and WMH
volume (estimate –0.012; p¼0.722) (Supplementary
Table 3).
Associations between LGCS and cognitive
domain changes
Executive functioning was estimated to decrease by
0.06 z-score units (95% CI, –0.08 to –0.05; p<0.001)
on average per year. For memory, mean decrease was
estimated at 0.06 z-score units (95% CI, –0.04 to –0.08;
p<0.001) per year for the study sample (Table 3).
At age 58 (i.e., intercept), LGCS versus no LGCS
was not associated with a lower z-score in executive
functioning (estimate –0.06; 95% CI, –0.20 to 0.09;
p¼0.403) or memory (estimate 0.05; 95% CI, –0.10
to 0.20; p¼0.496), and these estimates did not substan-
tially change after accounting for incident large brain
infarcts or lacunes (Table 3). Significant main effects of
sex, education level, and alcohol use were observed on
executive functioning and memory (Supplementary
Table 2).
LGCS, compared with no LGCS, was associated
with a greater decline in executive functioning by 0.02
z-score units (95% CI, –0.031 to –0.01; p<0.001;
Figure 1) per year. The association between LGCS
Table 2. Output of the linear mixed-effects models with age of patients at each visit as the time variable, neuroimaging outcomes as
dependent variables and low-grade carotid stenosis as independent variable.
BPF CSFF VF WMH
b
Estimate (95% CI) p-value Estimate (95% CI) p-value Estimate (95% CI) p-value Estimate (95% CI) p-value
Intercept
Model 1 79.6 (78.5 to 80.8) <0.001 18.4 (17.5 to 19.5) <0.001 1.99 (1.54 to 2.43) <0.001 –3.34 (–3.94 to –2.74) <0.001
Model 2 79.5 (78.3 to 80.6) <0.001 18.6 (17.5 to 19.6) <0.001 2.08 (1.64 to 2.52) <0.001 –3.21 (–3.81 to –2.61) <0.001
Time
Model 1 –0.25 (–0.28 to –0.22) <0.001 0.19 (0.16 to 0.22) <0.001 0.06 (0.05 to 0.07) <0.001 0.08 (0.07 to 0.10) <0.001
Model 2 –0.24 (–0.27 to –0.21) <0.001 0.19 (0.16 to 0.21) <0.001 0.06 (0.05 to 0.07) <0.001 0.08 (0.07 to 0.10) <0.001
LGCS
a
Model 1 0.07 (–0.30 to 0.40) 0.717 0.07 (–0.23 to 0.37) 0.661 –0.10 (–0.24 to 0.03) 0.134 0.00 (–0.18 to 0.17) 0.987
Model 2 0.04 (–031 to 0.37) 0.858 0.09 (–0.22 to 0.38) 0.591 –0.09 (–0.22 to 0.05) 0.200 0.03 (–0.15 to 0.20) 0.763
LGCS x Time
Model 1 –0.03 (–0.06 to –0.01) 0.002 0.03 (0.01 to 0.05) 0.011 0.01 (0.002 to 0.02) 0.019 0.01 (–0.03 to 0.02) 0.162
Model 2 –0.03 (–0.05 to –0.01) 0.004 0.02 (0.01 to 0.04) 0.014 0.01 (0.001 to 0.02) 0.026 0.01 (–0.03 to 0.02) 0.156
Model 1: adjusted for sex, large infarcts on MRI, lacunes on MRI, hypertension, diabetes mellitus, body mass index, smoking pack years and alcohol use
at baseline.
Model 2: model 1 with time-varying covariates for large infarcts and lacunes on MRI.
a
No LGCS as the reference category.
b
Natural log-transformed and standardized for total intracranial volume.
CI: confidence interval; LGCS: low-grade carotid stenosis; BPF: brain parenchymal fraction; CSFF: sulcal cerebrospinal fluid fraction; VF: ventricular
fraction; WMH: white matter hyperintensity volume.
314 Journal of Cerebral Blood Flow & Metabolism 43(2)
and change in executive functioning persisted after con-
trolling for incident large brain infarcts and lacunes
(Table 3). For memory, LGCS versus no LGCS was
associated with a greater decline by 0.012 z-score units
(95% CI, –0.02 to –0.001; p¼0.032; Figure 2) per year.
The association between LGCS and change in memory
slightly attenuated after controlling for incident large
brain infarcts and lacunes (Table 3).
When adding BPF as a time-varying predictor, we
observed that lower BPF was associated with a lower
z-score in executive functioning at baseline and follow-
up (estimate –0.05; 95% CI, –0.07 to –0.02; p¼0.001)
in a model that controlled for sex, large infarcts on
MRI, lacunes on MRI, hypertension, diabetes mellitus,
body mass index, smoking pack years and alcohol use
at baseline. In this model, LGCS (compared with no
LGCS) remained associated with a greater decline in
executive functioning by 0.01 z-score units (95% CI,
–0.02 to –0.01; p¼0.007) per year. Lower BPF was
associated with a lower z-score in memory at baseline
and follow-up, however the association was not signif-
icant (estimate –0.02; 95% CI, –0.05 to 0.01; p¼0.161).
In this model, we observed that LGCS (compared with
no LGCS) was associated with a greater decline in
memory by 0.01 z-score units per year, however the
association was not significant (95% CI, –0.02 to
0.00; p¼0.103).
In the joint model analysis, parameter estimates for
the time effect were comparable with the primary anal-
yses (Supplementary Table 3). Controlling for death/
dropout, LGCS remained associated with a greater
decline in executive functioning (estimate –0.017; 95%
CI, –0.026 to –0.01; p<0.001) and memory (estimate
–0.011; 95% CI, –0.017 to –0.004; p¼0.002). Estimates
of association parameters were significant for both
executive functioning (estimate –1.34; p<0.001) and
memory (estimate –0.546; p<0.001), indicating that
death/dropout impacted average change in executive
functioning and memory over time.
Discussion
In this cohort of patients with manifest arterial disease,
we observed that asymptomatic low-grade carotid
artery stenosis (LGCS) was associated with greater
progression of global, cortical and subcortical brain
atrophy, but not with white matter hyperintensities
(WMH) compared with absence of stenosis. LGCS
was also associated with a greater decline in executive
functioning and memory throughout the follow-up
period of 12 years. These relationships were indepen-
dent of demographics, cardiovascular risk factors and
brain infarcts on MRI.
In clinical practice, emphasis is on the detection of
carotid stenosis due to the associated risk of atheroem-
bolic stroke. The risk of atheroembolic stroke is rela-
tively low in LGCS but increases substantially in
moderate and severe stenosis.
3,4
The findings of this
long-term follow-up study, however, suggest that
asymptomatic LGCS may be of clinical importance
as a marker of greater future brain atrophy and cogni-
tive decline. Our results are consistent with a recent
cross-sectional study in which mild carotid atheroma
Table 3. Output of the linear mixed-effects models with age of patients at each visit as the time variable, cognition domain-specific
z-scores as dependent variables and low-grade carotid stenosis as independent variable.
Executive functioning Memory
Estimate (95% CI) p-value Estimate (95% CI p-value
Intercept
Model 1 –0.84 (–1.37 to –0.32) 0.002 –0.29 (–0.84 to 0.27) 0.311
Model 2 –0.78 (–1.31 to –0.25) 0.004 –0.30 (–0.86 to 0.26) 0.293
Time
Model 1 –0.06 (–0.08 to –0.05) <0.001 –0.06 (–0.04 to –0.08) <0.001
Model 2 –0.06 (–0.07 to –0.04) <0.001 –0.06 (–0.04 to –0.07) <0.001
LGCS
a
Model 1 –0.06 (–0.20 to 0.09) 0.403 0.05 (–0.10 to 0.20) 0.496
Model 2 –0.04 (–0.18 to 0.10) 0.577 0.04 (–0.11 to 0.19) 0.586
LGCS x Time
Model 1 –0.020 (–0.031 to –0.01) <0.001 –0.012 (–0.02 to –0.001) 0.032
Model 2 –0.017 (–0.028 to –0.006) 0.003 –0.010 (–0.02 to 0.001) 0.082
Model 1: adjusted for sex, education level, practice effect, large infarcts on MRI, lacunes on MRI, hypertension, diabetes mellitus, body mass index,
smoking pack years and alcohol use at baseline.
Model 2: model 1 with time-varying covariates for large infarcts and lacunes on MRI.
a
No LGCS as the reference category.
CI: confidence interval; LGCS: low-grade carotid stenosis.
Ghaznawi et al. 315
was related to cortical thinning and worse fluid
intelligence.
10
The exact mechanisms underlying the association of
LGCS with greater brain atrophy and cognitive decline
remain to be determined. One such mechanism may be
cerebral hypoperfusion secondary to LGCS, however
this explanation is less likely because carotid artery ste-
nosis <50% is usually considered hemodynamically
insignificant. Symptomatic or silent brain infarcts and
lacunes are also less likely to explain the observed rela-
tionships because we included only asymptomatic
patients with LGCS and we adjusted the analyses for
prevalent and incident silent brain infarcts on MRI. We
also observed that cardiovascular risk factors such as
diabetes mellitus or hypertension did not explain the
association of LGCS with greater brain atrophy or cog-
nitive decline, even though patients with LGCS did
show a less favorable cardiovascular profile at baseline.
In this context, it is possible that LGCS represents a
proxy marker for more profound atherosclerotic vas-
cular changes within the cerebrum or physiological
changes associated with more severe generalized ath-
erosclerosis such as low-grade systemic inflamma-
tion.
10,27
These processes, which may be difficult to
measure in patients, may negatively impact brain
health over time leading to greater brain atrophy and
cognitive decline.
To the best of our knowledge, no previous studies
compared trajectories of brain MRI changes and cog-
nitive functioning between asymptomatic patients with
LGCS and those without. Studies examining the
impact of carotid plaque (irrespective of degree of ste-
nosis) on cognitive functioning have reported conflict-
ing findings. In the Northern Manhattan Study,
carotid plaque was not related to greater cognitive
decline throughout the follow-up period of 5 years.
28
In the Tromsø Study, however, presence of carotid
plaque at baseline was associated with lower cognitive
test scores measured 7 years later.
29
With respect to
WMH, our findings are in line with a prospective anal-
ysis of the Rotterdam Scan Study in which increasing
carotid plaque severity was not associated with pro-
gression of WMH over 3 years of follow-up.
30
Limitations of this study include, first, the substan-
tial attrition during follow-up. However, we addressed
this issue by performing sensitivity analyses using joint
models and we observed that the relations between
LGCS, progression of brain atrophy and cognitive
decline held after controlling for death/dropout.
Second, cognitive testing in this analysis was limited
to only two cognitive domains. Third, as follow-up
measurements of vascular risk factors were available
only in a limited number of patients, our analyses did
not account for changes in vascular risk factors during
follow-up. Fourth, the volumetric MRI technique used
in our study did not allow us to measure region-specific
brain volume changes. Results from a recent cross-
sectional analysis in the Lothian Birth Cohort 1936
indicate that carotid atheroma was predominantly
associated with smaller volumes in specific anterior
and posterior cortical regions, whereas regions of the
primary motor and sensory cortex were relatively
spared.
10
Lastly, volumetry in this study was performed
on MRI sequences with a slice thickness of 4 mm
instead of 1 mm, which is likely more sensitive in
detecting brain volume changes.
Strengths of this study are the large number of
patients included, the long follow-up period and the
multiple brain MRI and cognitive functioning meas-
urements recorded over time. In addition, we
accounted for silent cerebrovascular disease on baseline
MRI in the analyses. Also, we used prospective MRI
data to adjust the analyses for incident brain infarcts
and lacunes during follow-up. Lastly, we also
accounted for a potential practice effect in the cognitive
analyses due to the relatively short interval between the
baseline and first follow-up measurement of 4 years.
Overall, our findings demonstrate that asymptomat-
ic LGCS is associated with greater cognitive decline
and greater progression of global, cortical, and subcor-
tical brain atrophy over 12 years of follow-up, indepen-
dent of demographics, cardiovascular risk factors, or
brain infarcts on MRI. These results indicate that
LGCS, a common finding in older individuals and
patients with manifest arterial disease, may be a clinical
marker of greater future brain atrophy and cognitive
decline.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: Funding for this paper was received as part of a grant
from the Netherlands Organization for Scientific Research-
Medical Sciences (NWO-MW: project No. 904-65-095). This
funding source had no role in the design, data collection, data
analyses and data interpretation of the study or writing of the
report. We also gratefully acknowledge the funding from the
European Research Council under the European Union’s
Horizon 2020 Programme (H2020)/ERC grant agreement
n637024 and n66681 (SVDs@target).
Acknowledgements
We gratefully acknowledge the contribution of the research
nurses; R. van Petersen (data-manager); B. van Dinther
(study manager) and the members of the Utrecht
Cardiovascular Cohort-Second Manifestations of ARTerial
disease-study group (UCC-SMART-study group): F.W.
Asselbergs and H.M. Nathoe, Department of Cardiology;
G.J. de Borst, Department of Vascular Surgery; M.L. Bots
and M.I. Geerlings, Julius Center for Health Sciences and
Primary Care; M.H. Emmelot, Department of Geriatrics; P.
316 Journal of Cerebral Blood Flow & Metabolism 43(2)
A. de Jong and T. Leiner, Department of Radiology; A.T.
Lely, Department of Obstetrics/Gynaecology; N.P. van der
Kaaij, Department of Cardiothoracic Surgery; L.J. Kappelle
and Y. Ruigrok, Department of Neurology; M.C. Verhaar,
Department of Nephrology, F.L.J. Visseren (chair) and J.
Westerink, Department of Vascular Medicine, University
Medical Center Utrecht and Utrecht University.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Authors’ contributions
1) conception and design of the study: RG, JV, JH, MIG
2) acquisition and analysis of data: RG, JV, MHTZ, JB, IR,
JH, MIG
3) drafting a significant portion of the manuscript or figures:
RG, JV, IR, MIG
ORCID iDs
Rashid Ghaznawi https://orcid.org/0000-0002-6616-5276
Maarten HT Zwartbol https://orcid.org/0000-0001-5779-
3150
Jeroen de Bresser https://orcid.org/0000-0003-0759-8407
Ina Rissanen https://orcid.org/0000-0002-6869-0437
Supplemental material
Supplemental material for this article is available online.
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