Clinical Sciences
Predictors of Clinical Worsening in Cerebral Autosomal
Dominant Arteriopathy With Subcortical Infarcts and
Leukoencephalopathy
Prospective Cohort Study
Hugues Chabriat, MD; Dominique Hervé, MD; Marco Duering, MD; Ophelia Godin, PhD;
Eric Jouvent, MD; Christian Opherk, MD; Nassira Alili, MD; Sonia Reyes, MSc;
Aude Jabouley, MSc; Nikola Zieren, PhD; Jean-Pierre Guichard, MD; Chahin Pachai, PhD;
Eric Vicaut, MD; Martin Dichgans, MD
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
Background and Purpose—Predictors of clinical worsening in cerebral autosomal dominant arteriopathy with subcortical
infarcts and leukoencephalopathy remain unknown. This study aims to identify demographic, clinical, and magnetic
resonance imaging predictors of incident strokes, incident dementia, clinical deterioration, and death in patients with this
genetically proven disease.
Methods—Two hundred ninety subjects (mean age, 50.6±11.4 years) were assessed at baseline and followed up for 36
months. Incident clinical events were recorded, and clinical scores included the Mini Mental State Examination, Mattis
Dementia Rating Scale, modified Rankin Scale, and Barthel index. The number of lacunes and microbleeds, the volume
of white-matter hyperintensities, and brain parenchymal fraction were assessed on baseline magnetic resonance imaging.
Data were analyzed by ANCOVA, multivariable logistic regression, and Cox proportional hazard models.
Results—Incident stroke occurred in 55 of 278 patients (19.8%). Moderate or severe disability developed in 19 of 210
(9%) nondisabled individuals, incident dementia in 49 of 231 (20%) nondemented subjects, and 4.8% of patients died.
Active smoking, the number of lacunes, and brain parenchymal fraction independently predicted incident stroke during
follow-up. Gait disturbance, dementia, and brain parenchymal fraction predicted progression toward moderate or severe
disability. Active smoking, disability, and brain parenchymal fraction predicted incident dementia. Age was the only
significant predictor of death.
Conclusions—Clinical assessment and brain magnetic resonance imaging aid in predicting incident clinical events and
clinical deterioration in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.
There is a bidirectional relationship between dementia and moderate or severe disability in predicting each other’s onset.
Active smoking is a modifiable risk factor associated with clinical progression in Notch3 mutation carriers. (Stroke. 2016;47:4-11. DOI: 10.1161/STROKEAHA.115.010696.)
Key Words: CADASIL ◼ cerebral small vessel diseases ◼ cohort studies ◼ magnetic resonance imaging
◼ risk factors
C
erebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an
inherited small vessel disease (SVD) caused by mutations in
the NOTCH3 gene whose prevalence may reach 10.7/100 000
in the general population.1,2 CADASIL has emerged as the
most frequent hereditary cause of stroke in adults and a major
cause of vascular cognitive impairment.3 The clinical course is
highly variable. Some patients develop severe manifestations
while still in their 20s, whereas others remain symptom free
until late age.3,4
Genotype–phenotype studies failed to identify a differential effect of individual Notch3 mutations on clinical
Received July 7, 2015; final revision received October 13, 2015; accepted October 16, 2015.
From the Department of Neurology, GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot
and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France (H.C., D.H, O.G., E.J., N.A., S.R., A.J.); INSERM UMR 1161, Paris, France (H.C., D.H., E.J.);
Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany (M.D., C.O., N.Z.,
M.D.); Department of Neuroradiology, CHU Lariboisière, Assistance Publique des Hôpitaux de Paris, Paris, France (J.-P.G.); Bioclinica Inc, Lyon, France
(C.P.); Unité de Recherche Clinique, GH Saint Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris, Université Paris Denis Diderot, Paris, France
(E.V.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (M.D.).
The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.
115.010696/-/DC1.
Correspondence to Hugues Chabriat, MD, Service de Neurologie, Hopital Lariboisiere, Université Paris 7, 2 rue A Paré, 75010 Paris, France. E-mail
[email protected]
© 2015 American Heart Association, Inc.
Stroke is available at http://stroke.ahajournals.org
DOI: 10.1161/STROKEAHA.115.010696
4
Chabriat et al Predictors in CADASIL 5
progression, and the only established risk factor for clinical
deterioration till now is age. Hence, counseling mutation carriers and selecting patients for clinical trials5 have been difficult. Given the paucity of prognostic data,6–10 we initiated
a prospective longitudinal study to identify the predictors of
clinical progression.
The clinical and imaging characteristics of CADASIL
much resemble those of the most severe forms of sporadic
SVD. We, thus, reasoned that information derived from a longitudinal study in CADASIL might offer insights relevant to
SVD in general. Here, we report the final results of this study
obtained in a large cohort of patients followed up for 3 years.
Methods
Study Cohort
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
Subjects were prospectively recruited between September 2003 and
April 2011 through 2 major referral centers for CADASIL (University
Hospital Lariboisière, Paris [n=178] and Ludwig Maximilians
Universität, Munich [n=112]). They were included regardless of
whether they had clinical manifestations of the disease if they were
at least 18 years of age, had a documented mutation in the NOTCH3
gene, and were willing to be followed up. Details of the study protocol have been reported elsewhere.11 A follow-up interval of 3 years
was chosen based on previous longitudinal data showing that significant changes in clinical scores can be detected within an interval of
2 years8 when following up patients seen at major referral centers and
to obtain a sufficient number of clinical events. In brief, clinical and
demographic data were collected at study entry and included age, sex,
years of education, and vascular risk factors (hypertension, diabetes
mellitus, hypercholesteremia, smoking, and alcohol use). Blood pressure was measured at baseline and at follow-up.
A history of transient ischemic attacks (focal symptoms lasting
<24 hours presumably of vascular origin) or stroke (rapidly evolving
focal symptoms lasting ≥24 hours with no apparent cause other than
of vascular origin), seizures, gait disturbance (any permanent difficulty to walk), psychiatric disorders (any psychological syndrome
leading to treatment or hospitalization), and dementia defined according to Diagnostic and Statistical Manual of Mental Disorders,
4th Edition criteria was obtained at baseline and at follow-up.
Subjects were interviewed and asked for cognitive complaints and
any permanent difficulties in gait and balance and underwent a detailed neurological and neuropsychological examination at baseline
and at 3 years. Global cognitive function was assessed using the
Mini Mental State Examination and the Mattis Dementia Rating
Scale (MDRS). The initiation/perseveration subscale of the MDRS,
highly sensitive to subcortical lesions in cerebral SVD,12 was analyzed separately. Disability was assessed using the modified Rankin
Scale (mRS). Functional independence was determined using the
Barthel index.
Written informed consent was obtained from the study participant
or a close relative if the patient was too severely disabled. This study
was approved by the ethics committees of both participating centers.
We used standardized procedures as recommended in therapeutic trials to continuously check both the validity and accuracy of data with
regular monitoring (online-only Data Supplement).13 Database verification, cleaning, and validation (double data entry, written queries for
all inconsistencies, and corrections) were performed by Orgametrie
Inc (Roubaix, France).
Native and preprocessed data were displayed on Unix workstations running image analysis software from Bioclinica Inc. Validated
methods were used by board-certified neurologists and neuroradiologists with a long experience in SVD imaging research for evaluating
white-matter hyperintensities (WMH), lacunes, and microbleeds as
previously described.11,14 The total volume of WMH was normalized
to the intracranial cavity (ICC; nWMH=[volume of WMH/volume
ICC]×100). Trained raters segmented lacunes manually using appropriate 2D and 3D imaging tools. In difficult cases, a consensus was
obtained. Special care was taken to exclude enlarged perivascular
spaces. Microbleeds were defined as rounded foci ≤5 mm in diameter hypointense on gradient-echo sequences. Brain volumes were
determined using SIENAX and T1-weighted images.15 Normalized
brain volumes corrected for skull size (brain parenchymal fraction
[BPF]=brain volume/ICC) were then used for statistical analyses.
Imaging variables were stratified according to their median value
at baseline (Table 1) to simplify the clinical interpretation as follows:
the number of lacunes was stratified into ≤3 and >3; microbleeds
were stratified into present and not present; and BPF was stratified
into ≥0.863 and <0.863.
Statistical Analysis
Changes of clinical scores (Mini Mental State Examination, Barthel
index, mRS, and MDRS) were determined by subtracting baseline
scores from scores obtained at 3-year follow-up. To account for missing data, we proceeded as follows: for patients alive at 3 years but
who did not return for follow-up because they were too severely
demented and dependent, global assessment of disability was performed by informant telephone interviews; for other missing data, we
used multiple imputation methods. Multiple imputed datasets were
generated using the PROC-MI procedure of SAS with the Markov
Chain Monte Carlo method. The imputation model included all variables in the analysis. We chose to generate 5 imputed datasets based
Table 1. Main Clinical and MRI Features at Baseline in the
290 Patients Included in the Cohort Study
Demographic Characteristics and Vascular Risk Factors
Age (y), mean±SD
50.6±11.4
Sex (female/male), n (%)
160 (55.2)/130 (44.8)
Hypertension, n (%)
55 (19)
Hypercholesterolemia, n (%)
110 (38)
Active smoking, n (%)
59 (20.3)
Alcohol consumption (>2 drinks/d), n (%)
23 (7.8)
Diabetes mellitus, n (%)
6 (2.1)
Main clinical manifestations, n (%)
Asymptomatic
13 (4.4)
Migraine with aura
113 (39)
TIA or stroke
190 (65.5)
Gait disturbance
87 (30)
Balance problems
86 (29.7)
Moderate or severe disability (mRS score of ≥3)
51 (18)
Dementia
39 (13.5)
Main MRI parameters, mean±SD, range, median
No. of lacunes
Magnetic Resonance Imaging Data
nWMH
Details on scanner characteristics and magnetic resonance imaging
(MRI) sequences have been reported elsewhere11 (online-only Data
Supplement). The protocol included millimetric 3-dimensional (3D)
T1-weighted images, axial slices of 5 mm thickness of fluid-attenuated inversion recovery images, and T2*-weighted gradient-echo
planar images.
No. of MB
Brain parenchymal fraction
4.9±6.1, 0–29, 3
0.07±0.05, 0.0005–0.24, 0.062
3.2±13.2, 0–141, 0
0.853±0.006, 0.653–0.965, 0.863
MB indicates mircobleeds; MRI, magnetic resonance imaging; mRS, modified
Rankin scale; nWMH, total volume of white-matter hyperintensities; and TIA,
transient ischemic attack.
6 Stroke January 2016
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
on simulation studies demonstrating little gain in statistical power for
higher numbers of imputations.
Incident strokes and combined events were counted irrespective
of whether the subject had already experienced a stroke. Age and sex
were always forced into the multivariable models. Imaging variables
were dichotomized according to their median values. In addition, we
included parameters with a P value of <0.20 in univariate analyses.
Three models were tested: a model that included all demographic and
clinical factors (model 1), a model that included only MRI parameters
(model 2), and a model that included all demographic and clinical
factors, as well as MRI parameters (model 3). The same analyses
were performed to identify factors associated with a composite end
point of incident stroke, moderate or severe disability, dementia, or
death. Predictors of death were analyzed using a Cox proportional
hazard model.
To examine factors (age, sex, history of hypertension, presence of
any previous vascular risk factor, history of stroke, mRS score of ≥3,
presence of gait disturbance, and presence of dementia) and imaging variables potentially associated with a change of clinical scores
during follow-up, ANCOVA was performed with adjustment for age
and sex. Additional adjustments for education level were made for
cognitive scores.
Tests were 2-sided, and the significance level was fixed at 5%.
All analyses were conducted using SAS (release 9.3; SAS Statistical
Institute, Cary, NC).
Results
Two hundred ninety patients were included in the study. They
came from >100 families, 52 families had ≥2 members represented in the study (range, 2–8), and only 7 of them had >4
members. Two hundred seventy two patients (93.8%) had clinical manifestations of the disease, and 13 were asymptomatic.
Forty six patients previously had transient ischemic attacks,
141 patients had at least 1 ischemic stroke (range, 1–8), only
1 individual had an intracerebral hemorrhage, and in 2 cases,
the type of previous stroke was undetermined. Twenty-three
patients had a positive history of both transient ischemic attacks
and ischemic stroke. The other main clinical characteristics are
detailed in Table 1. The mean (±SD) time to follow-up was 37
(±1.8) months. Fifty-four patients did not return for the followup visit for different reasons as communicated by the patient,
the spouses, close relatives, or treating physician and using all
medical records available: death (n=14), severe disability with
dementia (n=23), and an acute stroke at the time of follow-up
(n=1; Figure, a full account of the study profile). No follow-up
data were obtained in 12 patients.
Baseline Predictors of Clinical Events During 3
Years
Incident stroke (all ischemic) occurred in 55 of 278 patients
(19.8%); 49 (80.9%) of whom had a history of stroke. In
multivariable analyses considering demographic and clinical parameters (model 1), the following baseline factors were
found to be associated with incident stroke: gait disturbance, a
history of stroke, and active smoking. When considering MRI
parameters alone (model 2), 2 baseline factors were associated
with incident stroke: >3 lacunes and the presence of microbleeds. When all parameters were considered together (model
3), the following baseline factors were associated with incident strokes: >3 lacunes, active smoking, and a BPF of <0.863
(Table 2).
Incident dementia developed in 49 of 231 patients (21.2%)
who were not demented at baseline. In multivariable analyses, the following baseline factors were found to be associated
with incident dementia: model 1, age, an mRS score of ≥3,
active smoking, and male sex; model 2, a BPF of <0.863 and
the presence of microbleeds; model 3, an mRS score of ≥3, a
BPF of <0.863, and active smoking. Incident dementia was
not found to be associated with incident stroke during followup (8/46 [17.4%] versus 38/181 [20.9%]; P=0.42).
Moderate or severe disability (mRS score, ≥3) developed in 19 of 210 subjects (9%) with no or mild disability
at baseline. In multivariable analysis, the following baseline
factors were found to be associated with the development of
moderate or severe disability: dementia and gait disturbance
in model 1; a BPF of <0.863 in model 2 and all 3 factors in
model 3 (Table 3). No significant association between incident
stroke and the development of moderate or severe disability
during follow-up was observed (14/44 [31.8%] versus 37/191
[19.3%]; P=0.07).
Death occurred in 14 subjects (4.8%). In multivariable
analysis, age was the only baseline parameter associated with
Patients enrolled in the cohort
study over 3 years (n=290)
Deceased before 3 years (n=14)
Severe disability with dementia (n=23) or acute stroke (n=1)
at 3 years prohibiting long travel to the study center
Follow-up evaluation performed more than
3 months after planned end date (n=3)
Figure. Flowchart.
- Severe clinical status due to cancer (n=1)
- No willingness to attend additional evaluation (n=9)
- Loss to follow-up (n=3)
Patients having completed their
visit at 3 years (n=236)
Chabriat et al Predictors in CADASIL 7
Table 2. Summary of Demographic, Clinical, and MRI Predictors of Major Clinical Events During 3 Years
Incident Events
During 3 y
Stroke
Model 1
Model 2
Model 3
Clinical, Demographic, and Vascular Risk
Factors
MRI Markers
1+2
Predictors
OR (95% CI)
P Value
Predictors
OR (95% CI)
Gait disturbance
4.6 (1.5–14.8)
0.009
No of lacunes, >3
5.2 (2.3–11.9)
<0.0001 No. of lacunes, > 3
4.7 (1.9–11.6)
0.03
History of stroke
4.1 (1.7–9.8)
0.002
Presence of MB
2.5 (1.1–5.5)
<0.0001
Smoking
2.6 (1.1–6.1)
0.0008
2.2 (1.1–4.8)
0.03
BPF*
1.1 (1.0–1.2)
0.02
BPF*
28.3 (3.2–251.9)
0.003
Dementia
Out of prediction†
Active smoking
mRS score, ≥3
Dementia
222 (8.7–999.9) <0.0001
P Value
Gait disturbance 30.5 (4.5–205.4) <0.0001
Dementia
mRS score, ≥3
Smoking
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
Death
Combined
0.007
Male sex
3.2 (1.1–9.5)
0.03
Age
1.1 (1.0–1.1)
0.01
Age
1.1 (1.0–1.2)
Gait disturbance 16.6 (5.7–48.9)
0.007
OR (95% CI)
P Value
BPF*
18.2 (1.7–197.9)
0.02
Gait disturbance
5.2 (0.8–32.8)
0.009
BPF*
11.5 (3.9–34.1)
<0.0001
mRS score, ≥3
16.2 (3.9–68.1)
0.0001
Presence of MB
2.4 (1.0–5.7)
0.04
BPF*
6.7 (2.1–21.6)
0.001
Active smoking
3.7 (1.1–12.0)
0.03
25.9 (5.7–116.4) <0.0001
3.3 (1.3–8.6)
Predictors
None
<0.0001 No. of lacunes, >3
…
5.4 (2.8–10.4)
…
Not done
<0.0001 No. of lacunes, >3
…
…
4.9 (2.4–9.8)
<0.0001
Smoking
2.7 (1.3–5.9)
0.01
BPF*
3.3 (1.6–6.6)
0.001
Gait disturbance
2.8 (1.2–6.9)
0.02
History of stroke
2.0 (1.1–3.9)
0.04
Presence of MB
2.2 (1.1–4.6)
0.03
BPF*
2.4 (1.2–5.2)
0.02
BPF indicates brain parenchymal fraction; CI, confidence interval; MB, microbleeds; MRI, magnetic resonance imaging; mRS, modified Rankin Scale; and OR, odds ratio.
*BPF corresponds to BPF of <0.863, the median value obtained in the sample.
†Among 39 demented patient at entry, only 8 had an mRS score of <3 at baseline and 5 had an mRS score of ≥3 at 3 yr (62.5%). OR could not be estimated in the
model because of the small number of subjects (in the absence of detailed dating at occurrence of each event, OR values were calculated).
death (model 1). No MRI parameter was found to be independently associated with death (model 2), but all patients who
died had >3 lacunes and a BPF <0.862, the median value of
BPF in the whole sample.
The composite end point of incident stroke, incident
dementia, moderate or severe disability, or death was
reached in 124 (47%) of 265 individuals with complete
information for all end points. In multivariable analysis, the
following baseline factors were found to be associated with
the composite outcome at 3 years: model 1, gait disturbance,
active smoking, and a history of stroke; model 2, >3 lacunes, a BPF of <0.863, and the presence of microbleeds; and
model 3, >3 lacunes, gait disturbance, and a BPF of <0.863.
The results did not change when the analysis was restricted
to individuals with an mRS of <3 and without dementia at
baseline (n=208).
Baseline Predictors of Change in Clinical Scores
During 3 Years
Mean values of the total MDRS, the MDRS initiation/perseveration subscore, the Barthel index, and the mRS score all
significantly deteriorated in the study cohort during the 3-year
interval (Table I in the online-only Data Supplement).
Focusing on demographic and clinical parameters, the following baseline factors were independently associated with
a decline of the total MDRS score: age, mRS score of ≥3,
balance problems, gait disturbance, and dementia (Table 3).
The same parameters were associated with a decline in the
initiation/perseveration subscore of the MDRS. Adjustment
for educational level did not change the results. An mRS score
of ≥3, gait disturbance, and dementia were further associated
with a decline of the Barthel index. Incident stroke was not
found to be associated with cognitive score changes (Table II
in the online-only Data Supplement).
Focusing on MRI parameters, the following factors were
associated with a decline of the total MDRS score: the number
of lacunes, presence, and number of microbleeds, as well as
BPF at baseline. The same parameters were associated with
a worsening of the initiation/perseveration subscore of the
MDRS (Table 4). The presence and number of MB, as well as
the BPF, were further independently associated with a worsening of the mRS and a decline of the Barthel index.
When all potential clinical and MRI predictors were considered together in multivariable analysis, an mRS score of
>3, the number of lacunes, the presence of microbleeds, and
BPF all were independently associated with worsening of the
total MDRS score during follow-up (Table 5). The only significant predictor of worsening in the initiation/perseveration
subscore was an mRS score of ≥3.
Sensitivity analyses showed that these results remained
essentially unchanged when statistical analysis was restricted
to the 236 patients who received their clinical evaluation at 3
years (Tables III, IV, V, VI, and VII in the online-only Data
Supplement).
Discussion
The main findings from this largest ever prospective study
on Notch3 mutation carriers were as follows: first, a substantial proportion of adult patients with CADASIL experience major neurological end points or deteriorate clinically
within a period of 3 years. Second, clinical status is a major
independent predictor of subsequent clinical worsening.
Specifically, gait disturbance represents a strong and independent predictor of cognitive decline, and there is a bidirectional
8 Stroke January 2016
Table 3. Baseline Demographic and Clinical Predictors of Change in the Main Clinical Scores Between Baseline and Follow-Up
(36 Months)
ΔMDRS (Total)
ΔInitiation/Perseveration MDRS
Mean (SE)
P Value
Mean (SE)
Male
−6.0 (0.5)
0.13
−3.17 (0.32)
Female
−4.0 (0.6)
ΔBarthel Index
ΔModified Rankin Scale
P Value
Mean (SE)
P Value
Mean (SE)
P Value
0.13
−5.00 (0.6)
0.72
0.69 (0.02)
0.25
Sex (%)*
−2.18 (0.29)
−5.77 (0.5)
0.53 (0.02)
Age tertile†
1st (<47 y)
−0.78 (0.4)
2nd (<56 y)
−5.02 (0.4)
0.0001
−0.75 (0.12)
−2.29 (0.30)
0.003
−1.90 (0.3)
−4.76 (0.8)
0.002
0.40 (0.03)
0.59 (0.02)
3rd
−9.01 (0.9)
−4.88 (0.60)
−9.74 (1.0)
0.83 (0.04)
0.04
Migraine with aura (%)
No
−5.4 (0.5)
Yes
−4.3 (0.5)
0.38
−2.95 (0.34)
0.30
−2.23 (0.24)
−6.47 (0.6)
0.21
−3.66 (0.6)
0.60 (0.01)
0.79
0.64 (0.02)
Stroke (%)
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
No
−4.4 (0.5)
Yes
−5.3 (0.4)
0.49
−2.19 (0.27)
0.29
−2.92 (0.30)
−6.57 (1.0)
0.49
−4.79 (0.6)
0.71 (0.03)
0.35
0.56 (0.01)
Modified Rankin Scale score, ≥3 (%)
No
−2.8 (0.3)
Yes
−14.9 (1.4)
<0.0001
−1.62 (0.16)
0.0015
−7.62 (1.06)
−4.31 (0.5)
0.05
−10.5 (1.1)
nd
nd
nd
Balance problems (%)
No
−3.9 (0.3)
Yes
−7.5 (0.9)
0.0253
−2.29 (0.23)
0.08
−3.61 (0.42)
−2.91 (0.3)
0.06
−9.02 (1.2)
0.57 (0.02)
0.45
0.70 (0.02)
Gait disturbance (%)
No
−2.60 (0.3)
Yes
−10.5 (1.1)
<0.0001
−1.53 (0.15)
0.0002
−5.33 (0.61)
−3.03 (0.3)
0.004
−10.9 (1.1)
0.53 (0.02)
0.12
0.79 (0.01)
Dementia (%)
No
−2.88 (0.3)
Yes
−18.3 (1.8)
<0.0001
−2.03 (0.17)
−6.73 (1.06)
0.0041
−2.86 (0.3)
−21.4 (2.5)
0.0001
0.62 (0.01)
0.77
0.56 (0.02)
MDRS indicates Mattis Dementia Rating Scale; and nd, not done.
Where possible, variables were adjusted for age and sex:
*after adjustment for age only and
†after adjustment for sex only.
relationship between dementia and disability in predicting
each other’s onset. Third, the number of lacunes and brain
volume are major independent predictors of clinical decline.
Finally, active smoking is a modifiable risk factor for clinical
worsening.
Almost half of our patients experienced the composite end
point of incident stroke, incident dementia, moderate or severe
disability, or death within 3 years after study inclusion. The
most common event was incident stroke followed by incident
dementia and development of moderate or severe disability.
These findings refine observations from previous retrospective
studies4 and demonstrate that adult Notch3 mutation carriers
are at high risk of clinical worsening.
We found a history of stroke to be associated with a
4-fold increased risk of incident stroke. However, there was
no significant relationship between a history of stroke and
worsening of clinical scores during the 3-year interval. Also,
incident strokes were not associated with incident dementia
or a decline of cognitive scores although there was a trend for
an association between incident stroke and the development
of moderate or severe disability during follow-up. Together,
these findings indicate that although clinical strokes represent
a risk factor for future strokes, they are not the only factor
contributing to clinical worsening. Additional studies are warranted to determine whether the accumulation of so-called
silent infarcts or the development of cerebral atrophy are the
main source of clinical worsening during 3 years.16,17 Also,
the lack of association with age and hypertension further supports that the progression of the disease itself is the main contributor of clinical worsening in such a time frame although
investigations are still needed to evaluate the potential effect
of various blood pressure levels or fluctuations on the progression of the disease.
Among the most consistently found predictors of clinical
worsening was gait disturbance. We found the latter to predict a decline of global cognitive function, executive function,
and the Barthel score during follow-up. Also, gait disturbance
independently predicted the composite end point of incident
stroke, severe disability, dementia, or death in a model that
included clinical and demographic variables and in the full
model. This is in keeping with previous studies in elderly
subjects that found gait and balance to be associated with
Chabriat et al Predictors in CADASIL 9
Table 4. Baseline MRI Predictors of Change in the Main Clinical Scores Between Baseline and Follow-Up
ΔMDRS
ΔInitiation/Perseveration MDRS
ΔBarthel Index
ΔModified Rankin Scale
β (SE)
P Value
β (SE)
P Value
β (SE)
P Value
β (SE)
P Value
−0.026 (0.02)
0.28
−0.008 (0.009)
0.39
−0.041 (0.03)
0.18
−0.009 (0.001)
0.99
−0.88 (0.17)
<0.0001
−0.27 (0.06)
<0.0001
−0.34 (0.19)
0.07
0.02 (0.01)
0.19
No
−4.85 (0.48)
0.0005
0.001
−3.48 (0.33)
0.04
0.46 (0.02)
0.01
Yes
−11.7 (1.0)
Number
−0.31 (0.08)
MRI at Baseline
nWMH
No. of lacunes
Microbleeds (%)
BPF
1.07 (0.16)
1.83 (0.20)
−4.22 (0.43)
0.001
<0.0001
−8.93 (1.14)
−0.11 (0.05)
0.06
0.34 (0.07)
<0.0001
0.88 (0.03)
−0.44 (0.16)
0.03
0.57 (0.24)
0.02
0.01 (0.005)
−0.04 (0.01)
0.01
0.009
Variables were adjusted for age and sex. BPF indicates brain parenchymal fraction; MDRS, Mattis Dementia Rating Scale; MRI, magnetic resonance imaging; and
nWMH, total volume of white-matter hyperintensities.
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
cognitive decline and dementia.18,19 Gait disturbance can be
easily assessed and may thus prove useful to predict the risk
of clinical progression.
We further found a modified Rankin Scale score of ≥3 to
predict clinical worsening, in particular cognitive decline. The
mRS score of ≥3 independently predicted a decline in global
cognitive function and executive function, and it was associated with incident dementia both in a model that included
clinical and demographic variables and in the full model.
Interestingly, there was a reciprocal relationship; in that, the
presence of dementia independently predicted the progression
toward moderate or severe disability in a model that included
clinical and demographic variables and in the full model.
Together, these observations highlight a bidirectional relationship between disability as assessed by the mRS score and
dementia diagnosed by Diagnostic and Statistical Manual of
Mental Disorders, 4th Edition.
MRI markers also independently predicted new clinical events, cognitive decline, and disability in our patients.
Among the strongest and most consistent predictors was the
load of lacunes. Patients with >3 lacunes were found to have
≈5-fold increased risk of both incident stroke and of reaching
the composite end point of incident stroke, incident dementia,
moderate or severe disability, or death both in the full model
and when considering imaging parameters alone. The number of lacunes further predicted cognitive decline as assessed
by the total MDRS score and the executive subscore. Brain
atrophy emerged as another important predictor. A lower BPF
independently predicted incident stroke, incident dementia, the development of moderate or severe disability, and a
decline of all clinical scores. These results are in line with
previous cross-sectional data that found the number of lacunes
and cerebral atrophy to be the 2 main MRI markers associated
with clinical severity in CADASIL.16,20 Our findings extend
previous studies by showing that the same imaging parameters
also predict future clinical worsening and can thus be considered markers of active disease. Microbleeds whose clinical value remains debated in sporadic SVD21 showed some
predictive capacity in model 2 but not in the full model, suggesting that these lesions are probably less relevant for clinical
risk prediction.
This study indicates that active smoking more than doubles the risk of incident stroke and more than triples the risk
of incident dementia. This effect was strong and remained
significant when adjusting for potential clinical and imaging
confounders. Our findings agree with previous cross-sectional
data in patients with CADASIL,10 and active smoking has
been reported to also increase the risk of stroke in sporadic
SVD.22 Smoking has multiple effects on the microvasculature,
including altered nitric oxide signaling of calcium channels,23
depletion of the free pool of tissue-type plasminogen activator24 and enhanced free-radical production.25 In this study,
active smoking was also found to increase the risk of incident dementia independently of other potential risk factors,
whereas a previous history of stroke did not change this risk.
Smoking has also been shown to double the risk of dementia
Table 5. Multivariate Linear Regression Analysis Including Demographic, Clinical, and Magnetic
Resonance Imaging Predictors of Cognitive Decline Found Significant in Univariate Analysis
ΔMDRS (Total)
ΔInitiation/Perseveration MDRS
β (SE)
P Value
β (SE)
P Value
0.73 (1.6)
0.65
0.3 (0.6)
0.63
Age
−0.15 (0.08)
0.06
−0.05 (0.03)
0.12
Modified Rankin Scale score, ≥3
−12.6 (3.5)
0.003
−3.5 (1.4)
0.03
−3.7 (2.2)
0.09
−1.6 (0.8)
0.07
5.0 (2.7)
0.07
1.3 (1.0)
0.19
No. of lacunes
−0.37 (0.17)
0.04
−0.13 (0.06)
0.06
MB
−0.17 (0.07)
0.03
−0.08 (0.05)
0.17
BPF
0.31 (0.16)
0.05
0.11 (0.06)
0.07
Male sex
Balance problems
Gait disturbance
BPF indicates brain parenchymal fraction; MB, microbleeds; and MDRS, Mattis Dementia Rating Scale.
10 Stroke January 2016
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
in the general population and to alter white-matter microstructure,26 although direct effects on the microvasculature were
not always detected.27 Further investigations are needed to
determine the effects of smoking on the vasculature and brain
tissue in patients with CADASIL.
Specific strengths of this study include the prospective
design, the large number of most incident events available
for analysis, a centralized data management, external monitoring, and the use of validated protocols for image analysis following clinical trial standards. Limitations include
a relatively long period of recruitment, lack of analysis of
potential effects of drugs in use, and a substantial number of
patients lost to follow-up although this was accounted for in
sensitivity analyses. The bidirectional relationship between
disability and dementia in predicting each other onset may
in part result from aspects of disability contributing to the
diagnosis of dementia and vice versa. However, they do
represent different aspects of disease. Also, results on lowfrequency events such as death need to be interpreted with
caution.
In conclusion, this study demonstrates that the combination of clinical, demographic, and MRI markers may aid in
predicting the risk of clinical worsening in CADASIL and that
this risk is in part modifiable. These results may have implications for the counseling and management of subjects with
CADASIL and for the planning of future therapeutic trials.
Acknowledgments
We acknowledge all patients who participated in this research, their
families, and the association CADASIL France for their active collaboration, Jocelyne Ruffié and Solange Hello for their involvement
in the practical organization of the study, M.G. Bousser and A. Kurtz
for their advice and constant support during the study, and the Unité
de Recherche Clinique of Saint-Louis/Lariboisiere hospital, Paris
France (V. Jouis and L. Guery) for their technical support.
Sources of Funding
This study was supported by grants from the French Ministry of
Health (Regional and National PHRC AOR 02-001), Association
de Recherche en NEurologie Vasculaire, the Vascular Dementia
Research Foundation, and the Fondation Leducq (Transatlantic
Network of Excellence on the Pathogenesis of Small Vessel Disease
of the Brain; http://www.fondationleducq.org).
Disclosures
None.
References
1. Joutel A, Corpechot C, Ducros A, Vahedi K, Chabriat H, Mouton P,
et al. Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nature. 1996;383:707–710. doi:
10.1038/383707a0.
2. Moreton FC, Razvi SS, Davidson R, Muir KW. Changing clinical patterns and increasing prevalence in CADASIL. Acta Neurol Scand.
2014;130:197–203. doi: 10.1111/ane.12266.
3.Chabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser
MG. Cadasil. Lancet Neurol. 2009;8:643–653. doi: 10.1016/
S1474-4422(09)70127-9.
4. Opherk C, Peters N, Herzog J, Luedtke R, Dichgans M. Long-term
prognosis and causes of death in CADASIL: a retrospective study in
411 patients. Brain. 2004;127(Pt 11):2533–2539. doi: 10.1093/brain/
awh282.
5. Dichgans M, Markus HS, Salloway S, Verkkoniemi A, Moline M, Wang
Q, et al. Donepezil in patients with subcortical vascular cognitive impairment: a randomised double-blind trial in CADASIL. Lancet Neurol.
2008;7:310–318. doi: 10.1016/S1474-4422(08)70046-2.
6. Liem MK, Lesnik Oberstein SA, Haan J, van der Neut IL, Ferrari MD,
van Buchem MA, et al. MRI correlates of cognitive decline in CADASIL:
a 7-year follow-up study. Neurology. 2009;72:143–148. doi: 10.1212/01.
wnl.0000339038.65508.96.
7.Holtmannspötter M, Peters N, Opherk C, Martin D, Herzog J,
Brückmann H, et al. Diffusion magnetic resonance histograms as a
surrogate marker and predictor of disease progression in CADASIL: a
two-year follow-up study. Stroke. 2005;36:2559–2565. doi: 10.1161/01.
STR.0000189696.70989.a4.
8. Peters N, Herzog J, Opherk C, Dichgans M. A two-year clinical followup study in 80 CADASIL subjects: progression patterns and implications for clinical trials. Stroke. 2004;35:1603–1608. doi: 10.1161/01.
STR.0000131546.71733.f1.
9. Molko N, Pappata S, Mangin JF, Poupon F, LeBihan D, Bousser MG,
et al. Monitoring disease progression in CADASIL with diffusion magnetic resonance imaging: a study with whole brain histogram analysis.
Stroke. 2002;33:2902–2908.
10. Adib-Samii P, Brice G, Martin RJ, Markus HS. Clinical spectrum of
CADASIL and the effect of cardiovascular risk factors on phenotype:
study in 200 consecutively recruited individuals. Stroke. 2010;41:630–
634. doi: 10.1161/STROKEAHA.109.568402.
11. Viswanathan A, Guichard JP, Gschwendtner A, Buffon F, Cumurcuic R,
Boutron C, et al. Blood pressure and haemoglobin A1c are associated
with microhaemorrhage in CADASIL: a two-centre cohort study. Brain.
2006;129(Pt 9):2375–2383. doi: 10.1093/brain/awl177.
12. Mok VC, Wong A, Lam WW, Fan YH, Tang WK, Kwok T, et al. Cognitive
impairment and functional outcome after stroke associated with small
vessel disease. J Neurol Neurosurg Psychiatry. 2004;75:560–566.
13.ICH. International Conference on Harmonisation of Technical
Requirements for Registration of Pharmaceuticals for Human Use ICH
Harmonised Tripartite guideline. Guideline for Good Clinical Practice.
E6 (r1). http://www.ICH.org. 1996.
14. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne
R, et al; STandards for ReportIng Vascular changes on nEuroimaging
(STRIVE v1). Neuroimaging standards for research into small vessel
disease and its contribution to ageing and neurodegeneration. Lancet
Neurol. 2013;12:822–838. doi: 10.1016/S1474-4422(13)70124-8.
15. O’Sullivan M, Jouvent E, Saemann PG, Mangin JF, Viswanathan A,
Gschwendtner A, et al. Measurement of brain atrophy in subcortical
vascular disease: a comparison of different approaches and the impact
of ischaemic lesions. Neuroimage. 2008;43:312–320. doi: 10.1016/
j.neuroimage.2008.07.049.
16. Viswanathan A, Godin O, Jouvent E, O’Sullivan M, Gschwendtner A,
Peters N, et al. Impact of MRI markers in subcortical vascular dementia:
a multi-modal analysis in CADASIL. Neurobiol Aging. 2010;31:1629–
1636. doi: 10.1016/j.neurobiolaging.2008.09.001.
17. Jouvent E, Viswanathan A, Mangin JF, O’Sullivan M, Guichard JP,
Gschwendtner A, et al. Brain atrophy is related to lacunar lesions and tissue microstructural changes in CADASIL. Stroke. 2007;38:1786–1790.
doi: 10.1161/STROKEAHA.106.478263.
18. Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg
Psychiatry. 2007;78:929–935. doi: 10.1136/jnnp.2006.106914.
19. Marquis S, Moore MM, Howieson DB, Sexton G, Payami H, Kaye JA,
et al. Independent predictors of cognitive decline in healthy elderly persons. Arch Neurol. 2002;59:601–606.
20. Viswanathan A, Gschwendtner A, Guichard JP, Buffon F, Cumurciuc
R, O’Sullivan M, et al. Lacunar lesions are independently associated
with disability and cognitive impairment in CADASIL. Neurology.
2007;69:172–179. doi: 10.1212/01.wnl.0000265221.05610.70.
21. Martinez-Ramirez S, Greenberg SM, Viswanathan A. Cerebral microbleeds: overview and implications in cognitive impairment. Alzheimers
Res Ther. 2014;6:33. doi: 10.1186/alzrt263.
22. Bezerra DC, Sharrett AR, Matsushita K, Gottesman RF, Shibata D,
Mosley TH Jr, et al. Risk factors for lacune subtypes in the Atherosclerosis
Risk in Communities (ARIC) Study. Neurology. 2012;78:102–108.
doi: 10.1212/WNL.0b013e31823efc42.
23. Gerzanich V, Zhang F, West GA, Simard JM. Chronic nicotine alters
NO signaling of Ca(2+) channels in cerebral arterioles. Circ Res.
2001;88:359–365.
Chabriat et al Predictors in CADASIL 11
24.Wang L, Kittaka M, Sun N, Schreiber SS, Zlokovic BV. Chronic
nicotine treatment enhances focal ischemic brain injury and
depletes free pool of brain microvascular tissue plasminogen activator in rats. J Cereb Blood Flow Metab. 1997;17:136–146.
doi: 10.1097/00004647-199702000-00002.
25.Hanna ST. Nicotine effect on cardiovascular system and ion channels. J Cardiovasc Pharmacol. 2006;47:348–358. doi: 10.1097/01.
fjc.0000205984.13395.9e.
26. Gons RA, van Norden AG, de Laat KF, van Oudheusden LJ, van Uden
IW, Zwiers MP, et al. Cigarette smoking is associated with reduced
microstructural integrity of cerebral white matter. Brain. 2011;134
(Pt 7):2116–2124. doi: 10.1093/brain/awr145.
27. Court JA, Johnson M, Religa D, Keverne J, Kalaria R, Jaros E, et al.
Attenuation of Abeta deposition in the entorhinal cortex of normal
elderly individuals associated with tobacco smoking. Neuropathol Appl
Neurobiol. 2005;31:522–535. doi: 10.1111/j.1365-2990.2005.00674.x.
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
Predictors of Clinical Worsening in Cerebral Autosomal Dominant Arteriopathy With
Subcortical Infarcts and Leukoencephalopathy: Prospective Cohort Study
Hugues Chabriat, Dominique Hervé, Marco Duering, Ophelia Godin, Eric Jouvent, Christian
Opherk, Nassira Alili, Sonia Reyes, Aude Jabouley, Nikola Zieren, Jean-Pierre Guichard,
Chahin Pachai, Eric Vicaut and Martin Dichgans
Downloaded from http://stroke.ahajournals.org/ by guest on June 15, 2017
Stroke. 2016;47:4-11; originally published online November 17, 2015;
doi: 10.1161/STROKEAHA.115.010696
Stroke is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2015 American Heart Association, Inc. All rights reserved.
Print ISSN: 0039-2499. Online ISSN: 1524-4628
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://stroke.ahajournals.org/content/47/1/4
Data Supplement (unedited) at:
http://stroke.ahajournals.org/content/suppl/2015/11/17/STROKEAHA.115.010696.DC1
http://stroke.ahajournals.org/content/suppl/2016/12/20/STROKEAHA.115.010696.DC2
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
in Stroke can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office.
Once the online version of the published article for which permission is being requested is located, click
Request Permissions in the middle column of the Web page under Services. Further information about this
process is available in the Permissions and Rights Question and Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Stroke is online at:
http://stroke.ahajournals.org//subscriptions/
SUPPLEMENTAL MATERIAL
SUPPLEMENTAL METHODS
Detailed MRI Methods
MRI sequences: MRI scans were obtained on 1.5-T systems [Siemens Magnetom Vision (Munich) or General
Electric Medical Systems Signa (Paris and Munich)]. 3DT1 sequences (Munich Vision: repetition time/echo
time (TR/TE) = 11.4/4.4 ms, slice thickness = 1.2 mm, no interslice gap, in-plane resolution = 0.9 x 0.9 mm;
Munich Signa: TR/TE = 22/6 ms, slice thickness = 1 mm, no gap, 0.9 x 0.9 mm; Paris: TR/TE 9.1/2 ms slice
thickness = 0.8 mm, no gap, 1.02 x 1.02 mm), FLAIR (Munich Vision: repetition time/echo time/inversion time
(TR/TE/TI) = 4284/110/1428 ms, slice thickness 5 mm, no gap, 0.98 x 0.98 mm; Munich Signa: TR/TE/TI =
8402/151/2002 ms, slice thickness = 5 mm, no gap, 0.94 x 0.94 mm; Paris: TR/TE/TI = 8402/161/2002 ms,
slice thickness = 5.5 mm, no gap, 0.94 x 0.94 mm) and T2*-weighted gradient echo imaging (Munich Vision:
TR/TE = 1056/22 ms, slice thickness = 5 mm, no gap, 0.98 x 0.98 mm; Munich Signa: TR/TE = 1040/22 ms,
slice thickness = 5 mm, no gap, 0.94 x 0.94 mm Paris: TR/TE = 500/15 ms, slice thickness = 5.5 mm, no gap,
0.98 x 0.98 mm) were performed.
Image Analysis: Image analysis was performed using custom evaluation software from Bioclinica Inc (Lyon,
France)1. White matter hyperintensities were analyzed on FLAIR images. All FLAIR axial slices from the base
of the cerebellum to the vertex were assessed. A mask of lesions was generated from FLAIR images after
application of the intra cranial cavity (ICC) mask by applying a threshold on signal intensity derived from the
signal intensities histogram. The total volume of WMH was normalized to the ICC in each patient (nWMH =
[volume of WMH/volume ICC]*100). The method was shown to have a high interrater reliability (intraclass
correlation coefficient = 0.998).2 Lacunes were manually segmented using custom 2D and 3D image editing
tools. Hypointense lesions on T1-weighted images with a signal identical to CSF, sharp delineation and a
diameter >2 mm were segmented. Perivascular spaces were excluded by size <2 mm or their typical
orientation along perforating vessels 3. Particular attention was paid to brain regions typically containing
perivascular spaces. In difficult cases decisions were reached by consensus between experienced raters. The
total volume of lacunes in each patient was also normalized to the ICC (nLV = [volume of lacunes/volume
ICC]*100). The interrater reliability of assessment of the volume or the number of lacunes was good (intraclass
correlation coefficient = 0.830 and 0.824, respectively) 2. Microbleeds were defined as rounded foci ≤ 5 mm in
diameter hypointense on gradient-echo sequences and distinct from vascular flow voids, leptomeningeal
hemosiderosis, or non-hemorrhagic subcortical mineralization. The location and number of microbleeds were
recorded. It was agreed before the study that only lesions considered to be independent hemorrhagic foci
would be counted. Each lesion was marked electronically by the reader on the screen and all lesions were
recorded in the corresponding 3D space for each exam. All disagreements were resolved by consensus. High
interrater reliability for the number of CM has been previously shown (intraclass correlation coefficient = 0.964)
2. For obtaining brain volumes at baseline, the FSL-SIENAX algorithm 4, 5 was applied using T1-weigted
images as previously reported 6 based on the respective manually refined brain mask obtained in each
subject. Normalized brain volumes corrected for different skull sizes (or Brain Parenchymal Fraction (BPF) =
brain volume / intracranial cavity volume) were then used for statistical analyses.
1
Database verification, cleaning and validation
Data were collected using paper clinical research files (CRF). The validity of data in the CRF was
systematically checked with the source documents by clinical research assistants according to good clinical
practice (GCP) procedures (guideline for Good Clinical Practice from E6 International Conference on
Harmonization of technical requirements for registration of pharmaceuticals for human use, 1996). 7 The clinical
research organization (CRO) Orgametrie Inc was in charge of data entering using a double data entry method.
Data inconsistencies were then identified on the data base and queries were edited. Clinical research
assistants checked the answers to the queries made by the investigators and corrections were validated.
Sensitivity analysis
Data obtained in the subgroup of 236 patients who were evaluated at 3 years in each centre are
detailed in Tables III to VI.
Mean age of these subjects was 49.3 ± 10.9 (mean ± SD) years. At baseline, less than one in five
patients had a positive history of hypertension (n = 39; 17.7%), 39.6% had or were treated for
hypercholesterolemia (n = 87), 22.6% were current smokers (n = 49), only 8.5% had a significant alcohol
consumption (>2 drinks/day). Diabetes was reported in only 5 subjects (2.3%). The major manifestations of the
disease in the baseline sample were migraine with aura observed in 41.9% of patients (n = 99), TIAs and
stroke in 64% (n = 151), gait disturbances in 22.9% (n = 54), balance troubles in 23.7% (n = 54) and dementia
in 9.8% of subjects (n = 23). Thirteen percent of subjects (n= 30) presented with moderate or severe disability
(mRS ≥ 3). All these frequencies did not differ from those observed in the baseline sample of 378 individuals.
The results obtained without use of imputation for missing data are presented in the following tables
III, IV, V and VI.
The results obtained in Model 1b and Model 2b are close to those obtained in Model 1 and 2 in the
whole cohort of 290 patients using imputation methods for missing data.
2
SUPPLEMENTAL TABLES
Table I: Change of main clinical scores between baseline and follow-up (3 years)*
MMSE
mean (se)
MDRS (total)
mean (se)
MDRS
initiation/perseveration
mean (se)
mRankin Score (0-5)*
mean (se)
Barthel index
mean (se)
Baseline
FU (3 years)
Δ: FU – Baseline
p value
26.3 (0.3)
26.2 (0.3)
-0.1 (0.2)
0.57
131.2 (1.3)
126.3 (1.8)
-4.9 (0.7)
<0.0001
32.2 (0.5)
30.2 (0.6)
-2.0 (0.3)
<0.0001
1.0 (0.09)
1.7 (0.1)
0.7 (0.09)
<0.0001
90.6 (1.3)
85.5 (1.7)
-5.2 (1.1)
<0.0001
FU: Follow-up, * analysis was performed after using imputation techniques for replacing missing data (se =
standard error)
Table II: Relationships between incident stroke and cognitive scores between baseline and follow-up
(p* adjusted for age, sex and education level, note that the results did not change in the 290 patients
with using imputation methods for lost of follow-up)
Δ MDRS (total)
Incident stroke
Δ Init/persev MDRS
P*
No
-1.93 (0,6)
Yes
-0.95 (1.1)
0.44
Δ MMSE
P*
-1.08 (1.0)
-1.05 (2.0)
3
0,10
P*
0.25 (0.04)
0.13 (0.12)
0,77
Table III: Summary of demographic, clinical and MRI predictors of major clinical events over 3 years
Incident
events over 3
years
Stroke
Model 1b
Clinical, demographic, vascular risk factors
predictors
OR (95% CI)
p value
Gait disturbance
5.7 (1.8-18.3)
0.0004
History of stroke
3.5.1 (1.5-8.4)
0.0005
Active smoking
3.0 (1.4-6.7)
0.0005
predictors
N lacunes > 3
mRS≥3
Gait disturbance
<0.0001
BPF
0.8 (0.7-0.9)
0.0009
Dementia
mRS ≥ 3
Male sex
<0.0001
0.05
N lacunes
7.5 (3.2-17.6)
<0.0001
Death
Combined
mRS ≥ 3
Gait disturbance
Active smoking
History of stroke
0.04
0.0002
0.01
0.009
BPF
N lacunes > 3
Presence of MB
0.9 (0.8-0.9)
5.7 (2.7-12.0)
2.9 (1.3-6.6)
0.01
<0.0001
0.011
89.1 (19.9-397.8)
1.1 85.4 (5.7-999)
6.2 (1.0-38.3)
1.2
4.9 (1.1-23.1)
11.8 (3.2-43.2)
5.0 (2.7-12.0)
3.0 (1.3-6.9)
Model 2b
MRI markers
OR (95% CI)
7.5 (3.2-17.6)
p value
0.0004
Model 3b
Clinical, demographic, vascular risk factors and MRI markers
predictors
OR (95% CI)
p value
N lacunes > 3
3.8 (1.7-8.3)
0.0009
Dementia
Gait disturbance
BPF
mRS ≥ 3
Out of prediction*
15.6 (4.2-58.6)
0.8 (0.7-0.9)
1.3 54.2 (8.1-365.5)
Gait disturbance
Active smoking
N lacunes > 3
Presence of MB
5.0 (1.9-13.1)
2.2 (0.96-5.2)
4.9 (2.3-10.5)
2.3 (1.1-5.1)
<0.0001
0.0048
<0.0001
0.001
0.06
<0.0001
0.0331
*only 5 demented patients had mRS lower than 3 at baseline, 4 of them (80%) developed moderate or severe disability at 3 years. OR could not be estimated in
the model due to the small number of subjects.
4
Table IV : Baseline demographic and clinical predictors of change in the main clinical scores between
baseline and follow-up
SEX (%)
Male
Female
AGE TERTILE
1st (<47)
2nd (<56)
3rd
Δ MDRS (total)
MEAN (SE)
P
Δ Init/persev MDRS
Δ Rankin
MEAN (SE)
P
MEAN (SE) P
-5,3 (0,5)
-3.2 (0,8)
-2.11 (0,47)
-1.28 (0,33)
0,22
Δ Barthel
MEAN (SE) P
0,40 0.31 (0.005) 0,44 -2,00 (0.8) 0,65
0.24 (0.04)
-3,05 (0.8)
-0,69 (0.2) 0,0001 -0,48 (0,18) 0,003 0.27 (0.05) 0.72 -1,41 (0.8) 0.02
-3.85 (0.5)
-1.66 (0,24)
0.26 (0.06)
-2.83 (0.4)
-7.46 (0.8)
-2.70 (0,41)
0.28 (0.07)
-3.46 (0.8)
-4.5 (0.3) 0.60 -1.78 (0.16) 0.67 0.23 (0.05) 0.25 -2.66 (0.2) 0.85
MIGRAINE WITH AURA (%) -3.8 (0.6)
-1.54 (0.27)
0.36 (0.05)
-2.28 (0.8)
STROKE (%)
No
-3.5 (0.6) 0,47 -1.29 (0,13) 0,21 0.29 (0.04) 0,89 -3,02 (1,2) 0,75
Yes
-4.7 (0.6)
-1,93 (0,21)
0.27 (0.06)
-2.24 (0.3)
RANKIN ≥ 3 (%)
No
-2.5 (0,4) 0,009 -1.13 (0,15) 0,03
nd
nd -1.82 (0.5) 0,12
Yes
-13.4 (2,2)
-4,64 (1.01)
nd
-6.20 (1.2)
BALANCE PROBLEMS (%)
No
-3.4 (0.4) 0,0179 -1,61 (0,20) 0,67 0.30 (0.06) 0,54 -2,17 (0.9) 0,74
Yes
-6.1 (0.4)
1,88 (0,27
0.22 (0.05)
-3,33 (1.7)
GAIT DISTURBANCE (%)
No
-2,13 (0,3) 0,0001 -1.09 (0,21) 0,006 0.28 (0.05) 0,91 -1,30 (0.7) 0,14
Yes
-8.95 (0.8)
-3.04 (0,30)
0.27 (0.06)
-5.26 (1,0)
DEMENTIA (%)
No
-2,35 (0,3) 0,0012 -1,35 (0,16) 0,009 0.28 (0.04) 0,77 -0,85 (0.3) 0,003
Yes
-18,3 (2,3)
-4.26 (0.64)
0.22 (0.12)
-14.9 (2.5)
5
Table V: Baseline MRI predictors of change in the main clinical scores between baseline and follow-up
MRI at baseline
Δ MDRS
beta (se)
P
0,29
Δ Init/persev MDRS
beta (se)
P
-0,013 (0,01)
Number of lacunes
Micro-bleeds (%)
No
Yes
Number
-0.59 (0.10) <0.0001
-0.19 (0.06)
-3,23 (0,25)
-6,41 (0.56)
-0.15 (0,06)
1,43 (0,28)
-2,29 (0,48)
-0,01 (0,03)
0,10
0,69
-2.33 (0.49) 0,47 0,25 (0.03) 0,42
-4.19 (1.74)
0.36 (0.05)
-0,12 (0,27) 0,68 -0,01 (0,008) 0,17
BPF
0,65 (0,10) <0,0001
0,20 (0,07)
0,03
0,39 (0,26) 0,17
0,04
6
0,40
Δ Rankin
beta (se)
P
nWML
0,004
-0,006 (0,007)
Δ BARTHEL
beta (se)
P
-0,02 (0,02) 0,42 -0.002 (0.001) 0,15
0.0148 -0.25 (0.22) 0.27 0.001 (0.01) 0.93
-0.02 (0.01)
0,13
Table VI: Multivariate linear regression analysis including demographic, clinical and MRI predictors of
cognitive decline found significant in univariate analysis
Δ MDRS (total)
BETA (SE)
P
CLINICAL
MALE GENDER
AGE
RANKIN ≥ 3
BALANCE TRBLES
GAIT TRBLES
MRI
Number of lacune
MB
BPF
Δ Init/persev MDRS
BETA (SE)
P
1.5 (1.0)
-0.14 (0.05)
-5.9 (2.4)
0.9 (1.5)
-1.9 (1.8)
0.15
0.004
0.02
0.58
0.29
1.01 (1.8)
-0.06 (0.02)
-2.8 (1.2)
-0.32 (0.7)
0.03
0.01
0.02
0.66
-0.34 (0.10)
0.04 (0.09)
0.31 (0.12)
0.0008
0.68
0.009
-0.10 (0.05)
0.14 (0.05)
0.048
0.01
7
Table VII: Details of missing data
Demographic characteristics and vascular risk factors
Age (years, mean ±SD)
Sex (female/male)
Hypertension
Hypercholesterolemia
Active smoking
Alcohol consumption (>2 drinks/day)
Main clinical manifestations
Migraine with aura
TIA or stroke
Gait disturbance
Balance problems
Moderate or severe disability (mRS≥3)
Dementia
Main MRI parameters at baseline
Number of lacunes (mean ±SD, range, median)
nWMH (mean±SD, randge, median)
Number of MB (mean±SD, range, median)
Brain Parenchymal Fraction (mean ±SD, range, median)
MMSE at baseline
MDRS (total) at baseline
MDRS initiation at baseline
Rankin scale at baseline
Barthel score at baseline
MMSE at 36 month
MDRS (total) at 36 month
MDRS initiation at 36 month
Rankin scale at 36 month
Barthel scale at 36 month
No missing data
No missing data
No missing data
2 missing data
5 missing data
22 missing data
No missing data
No missing data
No missing data
No missing data
No missing data
No missing data
8 missing data
10 missing data
8 missing data
27 missing data
11 missing data
18 missing data
17 missing data
No missing data
1 missing data
68 missing data
81 missing data
79 missing data
No missing data
54 missing data
Note that data were missing for various reasons which are not necessarily related to the absence of visit
during follow-up (lack of information despite detailed interview and examination, insufficient quality for accurate
imaging quantification, severity of cognitive impairment or depression preventing some neuropsychological
measures, no direct examination at follow-up)
8
SUPPLEMENTAL REFERENCES
1.
2.
3.
4.
5.
6.
7.
Deloire MS, Salort E, Bonnet M, Arimone Y, Boudineau M, Amieva H, et al. Cognitive impairment as
marker of diffuse brain abnormalities in early relapsing remitting multiple sclerosis. Journal of
neurology, neurosurgery, and psychiatry. 2005;76:519-526
Viswanathan A, Guichard JP, Gschwendtner A, Buffon F, Cumurcuic R, Boutron C, et al. Blood
pressure and haemoglobin a1c are associated with microhaemorrhage in cadasil: A two-centre
cohort study. Brain : a journal of neurology. 2006;129:2375-2383
Doubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on
mri are a feature of cerebral small vessel disease. Stroke; a journal of cerebral circulation.
2010;41:450-454
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances
in functional and structural mr image analysis and implementation as fsl. NeuroImage. 2004;23
Suppl 1:S208-219
Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, et al. Accurate, robust, and
automated longitudinal and cross-sectional brain change analysis. NeuroImage. 2002;17:479-489
O'Sullivan M, Jouvent E, Saemann PG, Mangin JF, Viswanathan A, Gschwendtner A, et al.
Measurement of brain atrophy in subcortical vascular disease: A comparison of different
approaches and the impact of ischaemic lesions. NeuroImage. 2008;43:312-320
ICH. International conference on harmonisation of technical requirements for registration of
pharmaceuticals for human use ich harmonised tripartite cguideline. Guideline for good clinical
practice. E6 (r1). www.ICH.org. 1996
9
18
Stroke 日本語版 Vol. 11, No. 1
Abstract
皮質下梗塞および白質脳症を伴う常染色体優性脳動脈症
（CADASIL）の臨床的悪化の予測因子
―前向きコホート研究
Predictors of Clinical Worsening in Cerebral Autosomal Dominant Arteriopathy With
Subcortical Infarcts and Leukoencephalopathy
Prospective Cohort Study
Hugues Chabriat, MD1,2; Dominique Hervé, MD1,2; Marco Duering, MD3,4, et al.
1
Department of Neurology, GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and
DHU NeuroVasc Sorbonne Paris-Cité, Paris, France; 2INSERM UMR 1161, Paris, France; 3Institute for Stroke and Dementia Research, Klinikum
der Universität München, Ludwig-Maximilians-University, Munich, Germany; and 4Munich Cluster for Systems Neurology (SyNergy), Munich,
Germany
背景および目的：皮質下梗塞および白質脳症を伴う常染色
体優性遺伝性脳動脈症（ CADASIL ）の臨床的悪化の予測
因子はまだ明らかになっていない。本研究では，遺伝性で
あることが確認された本疾患を有する患者における脳卒中
および認知症の発症，臨床的悪化，死亡に関する人口統計
学的特徴，臨床特性，磁気共鳴画像（ MRI ）上の予測因子
を明らかにすることを目的とした。
方法：290 例の被験者（ 平均年齢 50.6±11.4 歳 ）をベース
ライン時および 36 ヵ月間の追跡調査時に評価した。臨床
イベントの発症を記録し，臨床スコアにはミニメンタル
ス テ ー ト 検 査（ MMSE ），Mattis Dementia Rating Scale
（MDRS），改変 Rankin Scale（mRS ），Barthel index を含
めた。ベースライン時の MRI でラクナ数および微小出血
数，白質高信号病変の体積，脳実質割合（ BPF ）を評価した。
データは ANCOVA，多変量ロジスティック回帰，Cox 比
例ハザードモデルにより解析した。
結果：脳卒中の発症は患者 278 例中 55 例（ 19.8％）で認め
られた。身体障害がなかった患者 210 例中 19 例（ 9％）に
中等度または重度の機能障害が出現し，認知症がなかっ
た患者 231 例中 49 例（ 20％）に認知症の発症が認められ，
4.8％の患者が死亡した。能動喫煙，ラクナ数，脳実質割
合は，追跡調査における脳卒中発症の独立した予測因子で
あった。歩行困難，認知症，脳実質割合は，中等度または
重度の機能障害への進行を予測した。能動喫煙，機能障害，
脳実質割合は，認知症の発症を予測した。死亡の有意な予
測因子は年齢のみであった。
結論： 臨床評価および脳 MRI は，CADASIL における臨
床イベントの発症および臨床的悪化の予測に有用である。
認知症と中等度または重度の機能障害は，相互の発症の
予測において双方向性の関連が認められる。能動喫煙は，
Notch3 変異保有者における臨床的進行に関連する是正可
能な危険因子である。
Stroke 2016; 47: 4-11. DOI: 10.1161/STROKEAHA.115.010696.
表 2 3 年間の主な臨床イベント出現に関する人口統計学的・臨床的予測因子，および MRI 上の予測因子
モデル 1
モデル 2
臨床的・人口統計学的危険因子，
および血管危険因子
3 年間におけるイベントの新規発症
予測因子
脳卒中
歩行困難
脳卒中の既往歴
能動喫煙
mRS スコア≧ 3
MRI マーカー
P値
予測因子
4.6（1.5 − 14.8 ）
0.009
ラクナ数＞ 3
4.1（1.7 − 9.8 ）
0.002
MB の存在
2.2（1.1 − 4.8 ）
0.03
OR（95％ CI）
認知症
222（8.7 − 999.9 ）＜ 0.0001
歩行困難
30.5（4.5 − 205.4 ）＜ 0.0001
モデル 3
BPF*
1＋2
P値
OR（ 95％ CI）
5.2（ 2.3 − 11.9 ） ＜ 0.0001
2.5（ 1.1 − 5.5 ）
＜ 0.0001
28.3（ 3.2 − 251.9 ）
0.003
予測因子
ラクナ数＞ 3
mRS スコア≧ 3 25.9（5.7 − 116.4 ）＜ 0.0001
0.03
喫煙
2.6（ 1.1 − 6.1 ）
0.0008
BPF*
1.1（ 1.0 − 1.2 ）
0.02
認知症
予測不能†
BPF*
喫煙
3.3（1.3 − 8.6 ）
0.007
男性
3.2（1.1 − 9.5 ）
0.03
年齢
1.1（1.0 − 1.1 ）
0.01
1.1（1.0 − 1.2 ）
0.007
死亡
年齢
複合
歩行困難
BPF*
MB の存在
なし
16.6（5.7 − 48.9 ） ＜ 0.0001 ラクナ数＞ 3
18.2（ 1.7 − 197.9 ） 0.02
5.2（ 0.8 − 32.8 ）
11.5（ 3.9 − 34.1 ） ＜ 0.0001 mRS スコア≧ 3 16.2（ 3.9 − 68.1 ）
2.4（ 1.0 − 5.7 ）
…
0.04
…
5.4（ 2.8 − 10.4 ） ＜ 0.0001
喫煙
2.7（1.3 − 5.9 ）
0.01
BPF*
3.3（ 1.6 − 6.6 ）
0.001
脳卒中の既往歴
2.0（1.1 − 3.9 ）
0.04
MB の存在
2.2（ 1.1 − 4.6 ）
0.03
P値
4.7（ 1.9 − 11.6 ）
歩行困難
認知症
OR（ 95％ CI）
0.009
0.0001
BPF*
6.7（ 2.1 − 21.6 ）
0.001
能動喫煙
3.7（ 1.1 − 12.0 ）
0.03
実施せず
ラクナ数＞ 3
…
…
4.9（ 2.4 − 9.8 ） ＜ 0.0001
歩行困難
2.8（ 1.2 − 6.9 ）
0.02
BPF*
2.4（ 1.2 − 5.2 ）
0.02
BPF：脳実質割合，CI：信頼区間，MB：微小出血，MRI：磁気共鳴画像，mRS：改変 Rankin Scale，OR：オッズ比。
*BPF は BPF ＜ 0.863（集団の中央値）を示す。
†登録時に認知症が認められた患者 39 例のうちベースライン時に mRS スコアが 3 未満であったのは 8 例のみであり，5 例では 3 年後の mRS スコアが 3 以上であった
（ 62.5％）。本モデルでは被験者数が少なかったため，OR は算出できなかった（各イベント出現の詳細な日付の記載がない場合は OR 値を計算した）。
PDLQLQGG
30