RESEARCH ARTICLE OPEN ACCESS

Prevalence of Fatigue and Associations With
Depression and Cognitive Impairment in Patients
With CADASIL
Amy A. Jolly,1 Success Anyanwu,2 Fatemeh Koohi,1 Robin G. Morris,3 and Hugh S. Markus1

Neurology® 2025;104:e213335. doi:10.1212/WNL.0000000000213335

Correspondence

Ms. Jolly

aj602@medschl.cam.ac.uk

Abstract
Background and Objectives
Fatigue is a common and disabling symptom in cerebrovascular disease and has been associated
with white matter damage, but the underlying disease mechanisms are poorly understood.
Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy
(CADASIL) is the most common genetic form of stroke and causes a cerebral small vessel
disease arteriopathy with white matter ischemia. We determined the prevalence of fatigue in
CADASIL, the factors associated with it, and its relationship with both depression and cognitive
impairment.

Methods
Prospectively recruited genetically confirmed patients with CADASIL were assessed using the
Fatigue Severity Scale. The prevalence of fatigue in CADASIL was compared with that of
healthy controls from the community. We determined associations between fatigue and clinical
features, cardiovascular risk factors, MRI parameters, cognition, and depression. Cognition was
measured using the Brief Memory and Executive Test (BMET) and depression using the
Geriatric Depression Scale (GDS). Mediation and path analyses were performed to determine
relationships between fatigue, depression, and cognitive impairment.

Results
One hundred seventy-four patients with CADASIL (mean age [SD] of 51.3 [12.30] years,
59.66% female) and 50 healthy controls were included in the analysis (mean age [SD] of 51.42
[12.58] years, 38.0% female). Fatigue was present in 51.7% of patients with CADASIL and was
almost 5 times more common than in controls (OR: 4.99, 95% CI [2.28–10.95], p < 0.001).
There was no association of fatigue with history of stroke or MRI parameters including white
matter hyperintensity lesion volume. Logistic regression showed both GDS total score (OR:
1.11 [1.05–1.17], p = 0.0002) and BMET total score (OR: 0.86 [0.75–0.98], p = 0.02) to be
predictors of fatigue. Fatigue, depression, and cognition were frequently comorbid. Mediation
analysis showed depression to have a greater effect on fatigue prevalence than cognitive im-
pairment. Path analysis confirmed depression to be the largest predictor of fatigue and found
this relationship to be bidirectional.

Discussion
Fatigue was present in over half of the patients with CADASIL. Depression and cognition were
the main predictors of fatigue, and all 3 symptoms were frequently comorbid. The relationship
between depression and fatigue was the strongest and was bidirectional. This suggests targeting
depressive symptoms may have benefit in fatigue management.

1Department of Clinical Neurosciences, University of Cambridge, United Kingdom; 2School of Clinical Medicine, University of Cambridge, United Kingdom; and 3King’s College Institute
of Psychiatry, Psychology and Neuroscience, London, United Kingdom.

The Article Processing Charge was funded by University of Cambridge Read and Publish deal.

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.

Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
e213335(1)

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Introduction
Fatigue is a common symptom after stroke,1,2 estimated to
affect up to 50% of stroke survivors.3,4 Despite this, the un-
derlying mechanisms are poorly understood, leading to few
effective therapies.5 Fatigue is also commonly seen in neu-
rologic diseases affecting the white matter, such as multiple
sclerosis, and has been reported to be common in cerebral
small vessel disease (SVD).6,7 A recent systematic review of
neuropsychiatric features in SVD found white matter hyper-
intensities (WMHs), a characteristic MRI feature of SVD, to
be associated with fatigue.7

Cerebral autosomal dominant arteriopathy with subcortical
infarcts and leukoencephalopathy (CADASIL) is the most
common monogenic form of stroke.8,9 CADASIL causes
a small vessel arteriopathy resulting in SVD with prominent
involvement of the white matter and the presence of both
lacunar infarcts and confluent WMHs.9 Clinical features of
CADASIL include migraine with aura, stroke, and cognitive
impairment and dementia.8,9 Case studies have reported fa-
tigue as a symptom in CADASIL,10-12 but little systematic
research has investigated its prevalence and risk factors. In-
vestigation of fatigue in CADASIL may also give insights into
the mechanism of the association between fatigue and white
matter disease.

Fatigue is highly associated with other neuropsychiatric
symptoms of which the most commonly cited is depression,
with the 2 symptoms being reported to be frequently
comorbid.2,13 Cognitive impairment has also been associated
with the presence of fatigue after stroke.2 Notably, depression
and cognitive impairment are also common symptoms in
CADASIL.8,14,15 How fatigue, depression, and cognitive im-
pairment inter-relate and whether either depression or cog-
nitive impairment plays a role in the pathogenesis of fatigue
remain uncertain.

To investigate this further, we determined the prevalence of
fatigue in CADASIL and its association with clinical and
demographic factors, as well as MRI markers of SVD. To
better understand the association between fatigue and neu-
ropsychiatric symptoms, we also determined the relationship
of fatigue with depression and cognitive impairment and
examined the inter-relationships between the 3 using path
analysis.

Methods
Participants

CADASIL Population
The CADASIL population analyzed were patients recruited to
the UK Familial Cerebral Small Vessel Disease (FSVD) study,
a prospective cohort study recruiting those with suspected
monogenic SVD from 5 UK centers (mentioned in Acknowl-
edgments). Medical history and family history were collected,
as well as the original clinical MRI scans.

Control Population
Patients with CADASIL were compared with controls
recruited as part of the Fatigue in Healthy Controls Study.
Controls were recruited from the community by way of ad-
vertisement flyers through social media, local groups, and
charities. Mood testing and cognitive testing were conducted
using the same measures performed in the CADASIL cohort.

Measures

Demographics and Medical History
Demographics including age, sex, and vascular risk factors
were collected for both CADASIL cases and controls. For
CADASIL participants, medical history was also collected
including commonly reported symptoms in CADASIL: mi-
graine, stroke, encephalopathy, psychiatric disorders, and
cognitive decline. If any features were present, age at occur-
rence and details of the symptom were recorded.

Fatigue Severity Scale (FSS)
The FSS is a brief measure of fatigue that assesses the in-
fluence of fatigue on daily behaviors.16 The measure has 9
questions in which respondents are required to indicate on
a 7-point Likert scale ranging from ‘strongly disagree’ to
‘strongly agree’ in response to statements such as ‘I am easily
fatigued.’16 The FSS also includes the Visual Analog Fatigue
(VAF) Scale, which asks patients to place their overall fatigue
on a scale of 0–10. To classify fatigue, the scores of the first 9
questions are averaged, and a score of ≥4 has been taken to
indicate significant fatigue.17,18

Geriatric Depression Scale (GDS)
The GDS is a short depression-screening questionnaire that
requires patients to answer yes/no to 30 statements.19 A score
of 0–9 indicates within normal range, 10–19 mild depressive

Glossary
BMET = Brief Memory and Executive Test; CADASIL = cerebral autosomal dominant arteriopathy with subcortical infarcts
and leukoencephalopathy;CMBs = cerebral microbleeds; EF/PS = Executive Functioning and Processing Speed; FSS = Fatigue
Severity Scale; GDS = Geriatric Depression Scale; NBV = normalized brain volume; O/M = Orientation and Memory;
RMSEA = root mean square error of approximation; SRMR = standardized root mean residual; SVD = small vessel disease;
VAF = Visual Analog Fatigue; VCI = vascular cognitive impairment; WMHs = white matter hyperintensities.

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symptoms, and 20–30 severe depressive symptoms.19 The
GDS has successfully been used in previous CADASIL pop-
ulation studies.20 The GDS total score can subdivided into
apathy, anxiety, and fatigue subscales.21 The maximum score
on the anxiety and apathy subscales is 10 while the maximum
score on the fatigue subscale is 9.21 Higher scores indicate
higher reported levels of the feature measured by the
subscale.21

Brief Memory and Executive Test (BMET)
The BMET is a brief cognitive screening tool designed for, and
shown to be sensitive to, cognitive impairment in SVD.22,23

The total maximum score on the BMET is 16, which can be
broken down into 2 subscales: Executive Functioning and
Processing Speed (EF/PS) (maximum score of 8) and Ori-
entation and Memory (O/M) (maximum score of 8) as pre-
viously described.22,23 A previously validated cutoff score
of ≤13 was taken to indicate vascular cognitive impairment
(VCI).23

Neuroimaging Features
Original clinical MRI scans of patients with CADASIL were
analyzed. If a patient hadmultiple scans, themost recent at the
time of recruitment was used. T1-weighted, gradient echo
(GE), susceptibility-weighted imaging (SWI), diffusion-
weighted imaging (DWI), and fluid-attenuated inversion re-
covery (FLAIR) sequences were required for analysis of
neuroimaging features.

Normalized Brain Volume
SIENAX (from FSL software, fsl.fmrib.ox.ac.uk24,25) was
used to estimate total brain volume, subdivided into white
matter and gray matter, with T1 sequences. Brain volumes
accounted for estimated skull size with a visual scaling fac-
tor (VSF).

Cerebral Microbleeds
Cerebral microbleeds (CMBs) were measured using the Brain
Observer Microbleed Rating Scale26 on GE or SWI sequen-
ces. CMBs were rated by an expert neurologist rater.

Lacune Count
A neurologist expert rater counted the number of lacunar
infarcts including both acute infarcts using DWI and old
cavitated lacunar infarcts using T1 and FLAIR sequences. A
lacune was defined as a subcortical infarct between 3 and
15 mm in diameter.27

Lesion Volume
Trained raters quantified WMHs using the semiautomated
contouring tool in Jim image analysis software, version 8
(Xinapse Systems28). WMHs were defined as areas on FLAIR
images showing increased signal. WMHs were normalized for
skull size using the VSF produced by SIENAX. Inter-rater
agreement between the 2 trained raters was good with an
intraclass correlation coefficient of 0.96, calculated from
a subset (n = 10) of the data.

Standard Protocol Approval and Consent
The FSVD Study was approved by the East of England Cam-
bridge Central Research Ethics Committee (16/EE/0118).
The Fatigue in Healthy Controls Study was approved by the
University of Cambridge Psychology Research Committee
(PREC.2022.054). All participants gave written informed
consent. For those without capacity in the FSVD Study,
a consultee gave written informed consent.

Statistical Analyses
Participants were classified as having fatigue if their average
FSS score was ≥4, as previously defined.17,18 Depressive
symptoms were assessed as a continuous variable using the
GDS total score and also binarized into depressed and not
depressed by collapsing the mild and severe depressive groups
into the depressive group (score of 10+). The GDS total score
was also subdivided into anxiety, apathy, and fatigue subscales
as previously described.21 Cognition was assessed as a con-
tinuous variable using the BMET total score and the EF/PS
and O/M subscales. VCI was determined by the BMET using
a cutoff of ≤13 as previously described.22,23

Differences in demographics and clinical characteristics, cog-
nitive test scores, and fatigue and GDS scores were compared
between the subgroups (patients with CADASIL and controls;
patients with CADASIL with and without fatigue), using the χ2

test, t tests, and logistic regression, where appropriate. All re-
gression analyses were controlled for age and sex. Any signifi-
cant associations were entered into logistic regression to
identify independent predictors of fatigue prevalence.

Owing to non-normal distributions, all MRI parameters were
normalized. Brain volume was normalized using the square
transformation. Lesion volume and lacune count were nor-
malized using the square root transformation, and CMBs were
normalized using a logarithm transformation. Only 117 par-
ticipants had sequences for analysis of CMBs; therefore, these
were excluded from the main analysis and analyzed separately
to retain sample size. MRI parameters were compared be-
tween fatigued and nonfatigued patients with CADASIL using
logistic regression, while controlling for age and sex.

Correlations between the average FSS score and cognitive
behavioral symptoms (cognitive score and depression score)
were performed using the Spearman rank test. Venn diagram
was used to display the comorbidity of cognitive, depressive,
and fatigue symptoms.

Mediation analyses were then performed for each symptom
using the binarized measures.

Finally, path analysis was performed using the R package
“Lavaan” (version 0.6-15)29 to identify a model able to best
explain direct and indirect effects of cognition, depression,
and fatigue on one another. Multiple models were modeled,
and only the best fitting was reported and displayed

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graphically. A sample of 153 participants with complete
data were selected for this.

Model fit was assessed using the model test statistic and 3
measures of model fit.30 For the model to be accepted as
accurate, the p value for the χ2 test must be p ≥ 0.05.30 The
root mean square error of approximation (RMSEA) and 90%
CIs were also checked; the RMSEA must be 0.05–0.08 (ac-
ceptable) or ≤0.05 (excellent).31 The comparative fit index
(CFI) is a score from 0 to 1, with a cutoff of ≥0.95 used to
determine good model fit.30,32 Finally, the standardized root
mean residual (SRMR) value was assessed, with a value
of ≤0.08 used to determine good model fit.32 Robust versions
of the model fit statistics were used where possible to account
for non-normal data.

Results
A total of 359 patients have been recruited to the FSVD Study
to date; of these, 251 had genetically confirmedCADASIL with
typical cysteine-changing variants and were included. Assess-
ment of fatigue was only added as an amendment part way
through the study; as such, 174 had fatigue testing and were
included in analyses. Of these 174, 170 had depression testing
and 169 had cognitive testing (eTable 1). Fifty healthy controls
with no history of serious medical history were included.

Comparison of Patients With CADASIL
and Controls
There were no significant differences in demographics or
vascular risk factors between the CADASIL and the control
groups (Table 1). The CADASIL group had lower scores on
the BMET EF/PS index, but not on the O/M index23

(Table 1). The CADASIL group scored higher on the FSS
and GDS compared with the control group (Table 1).

Prevalence of Fatigue
In the CADASIL group, 51.7% (90/174) were classed as
having fatigue compared with only 8.0% (4/50) of the con-
trols. On logistic regression, patients with CADASIL were
about 5 times more likely to have fatigue on the FSS than
controls (OR: 4.99, 95% CI [2.28–10.95], p < 0.001).

Between-Group Comparison
Fatigue was present in the CADASIL group in all age groups
(eFigure 1), and there was no difference in age between the
fatigued and nonfatigued CADASIL groups (Table 2). Ad-
ditional Spearman correlation showed no correlation between
age and the average fatigue score on FSS (ρ = 0.12, p = 0.11).

Within the CADASIL group, the presence of fatigue was asso-
ciated with a higher prevalence of depression and lower cog-
nitive scores, including for both EF/PS and O/M subdomains
(Table 2). There were no other differences in demographics or
clinical features including stroke prevalence between the fa-
tigued and nonfatigued groups (Table 2).Within the CADASIL
cohort, there were no differences in any MRI parameter (brain

volume, WMH volume, lacune count, or CMB count) between
those with and without fatigue (Table 2).

Logistic Regression
Significant variables (BMET score and GDS score) were en-
tered into logistic regression while controlling for age and sex.

On logistic regression, while controlling for age and sex, both
GDS total score (OR: 1.11 [1.05–1.17], p = 0.0002) and
BMET total score (OR: 0.86 [0.75–0.98], p = 0.02) were
predictors of FSS fatigue grouping.

Relationships Among Fatigue, Depression, and
Cognitive Behavioral Symptoms
There was much comorbidity between binarized GDS de-
pression, BMET VCI, and FSS fatigue symptoms (Figure 1).
The most common presentation was with all 3 symptoms
(n = 29, 16.67%), followed by comorbid fatigue and de-
pression (n = 23, 13.22%) (Figure 1). There was a significant
correlation between the average fatigue score and the GDS
total score (Spearman ρ = 0.45, p < 0.001) and BMET total
score (ρ = −0.31, p < 0.001). There was also a significant
correlation between the GDS total score and BMET total
score (ρ = −0.28, p < 0.001).

Mediation Analyses
Mediation analysis was performed to explore the effects of
depression and cognition on fatigue and to investigate
whether the effect of depression on fatigue was mediated by
cognition and, conversely, whether the effect of cognition on
fatigue was mediated by depression. The analysis showed
depression to have a larger direct effect on fatigue presence
than cognitive impairment (Figure 2; c’ = 0.38 vs cognitive
impairment: c’ = 0.16). The effect of depression on fatigue
was direct and not mediated through cognition (ab = 0.03,
95% CI [0.00–0.08]) (Figure 2A). By contrast, the effect of
VCI on fatigue prevalence was mediated through depression
(ab = 0.07, 95% CI [0.01–0.15]) (Figure 2B).

Path Analyses

Model Fit
The derived model is shown in Figure 3. All necessary model
fit values were met; the χ2 p value was 0.29, exceeding 0.05 in
line with recommendations.30 CFI and robust CFI were both
0.99, indicating good model fit30,32; the robust RMSEA was
0.04 [0.00–0.13] indicative of excellent model fit,31 and the
SRMR was 0.03, again below the threshold of ≤0.08 and
indicative of good model fit.32

Direct and Indirect Effects
In the model, there was a significant positive direct effect of
the average FSS score on the GDS score (β = 0.55, p = 0.004)
(Figure 3, Table 3). This relationship was bidirectional, and
there was also a significant and positive direct effect of the
GDS score on the average FSS score (β = 0.79, p = 0.05)
(Figure 3, Table 3). The effect of depression on fatigue was
slightly stronger than vice versa (Figure 3, Table 3). There

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was a significant total effect of fatigue on cognition, but this
was not explained by the direct or indirect effects of fatigue on
cognition (Table 3).

Correlations
There was a significant positive correlation between the
modified Rankin Scale score (disability) and age (p < 0.001)
(Figure 3, eTable 2). A significant positive correlation was also
observed between the fatigue score and depression score
(p = 0.04) (eTable 2).

Cognitive Subdomains
Additional analyses were undertaken to assess the relationship
between the cognitive subdomains and fatigue.

On mediation testing the effects of EF/PS subscale score on
fatigue with depression as a mediator, the EF/PS score had
a total effect on fatigue (c = −0.2, p = 0.02), which was lost
when removing the effects of depression (c’ = −0.01, p = 0.21).
There was also an indirect effect of the EF/PS subscale score on
fatigue through depression (ab = −0.01, 95%CIs [-0.2, −0.02]).

A further mediation assessing effects of O/M subscale score
and depression on fatigue showed an effect of the O/M
subscale score on fatigue (eFigure 2A: total effect (c = −0.27,
p < 0.001) and a direct effect (c’ = −0.22, p = 0.003)), which
was not mediated through depression.

A separate mediation showed no significant total (c = −0.13,
p = 0.12) or direct (c = −0.01, p = 0.92) effect of the O/M
score on depression. On the contrary, a stronger effect of the
EF/PS subscale score on depression was observed (c = −0.24,
p = 0.003), which persisted after removing effects of fatigue
(c’ = −0.16, p = 0.04) and was also mediated through fatigue
(ab = −0.08, 95% CIs [-0.15, −0.01]) (eFigure 2B).

Discussion
In this large cohort of patients with CADASIL, we found that
fatigue was common, being present in just over half of the
participants. This was 5 times more common than in controls.
The major predictors of fatigue were depressive symptoms

Table 1 Comparison of Demographics and Clinical Features of Patients With CADASIL and Controls

Population

CADASIL (n = 174) Controls (n = 50) Comparison

Demographic and cardiovascular risk factors

Age at clinic (mean, SD, min-max) 51.43, 12.47, 22–77 51.42, 12.58, 19–72 p = 1.00

Sex (% male) 71 (40.34) 19 (38.0) p = 0.89

History of hypertension (% yes) 45 (25.57) 9 (18.0) p = 0.27

Diabetes mellitus (% yes) 11 (6.25) 7 (14.0) p = 0.08

History of ever smoking (% yes) 59 (33.52) 15 (30.0) p = 0.63

Modified Rankin Scale score (median, IQR, range)c 0, 1, 0–4 0, 0, 0–0 p = 0.98

Cognitive measures

BMET total score (median, IQR, min-max) 14, 4, 2–16 15, 3, 8–16 p = 0.02a

EF/PS score (median, IQR, min-max) 8, 2, 0–8 8, 0, 3–8 p = 0.007b

O/M score (median, IQR, min-max) 7, 3, 0–8 7, 2, 2–8 p = 0.21

VCI on BMET (% yes)c 69 (40.35) 13 (28.26) p = 0.14

Depression measures

History of depression (% yes) 49 (27.84) 7 (14.0) p = 0.05a

GDS total score (mean, SD, min-max)c 9.97, 7.53, 0–30 4.60, 3.97, 0–18 p < 0.001b

Fatigue measures

FSS fatigue 84 (48.28) 9 (18.0) p < 0.001b

Average FSS score (mean, SD, min-max) 4.09, 1.69, 1–7 2.86, 1.06, 1.11–5.11 p < 0.001b

Abbreviations: BMET = Brief Memory and Executive Test; EF/PS = Executive Functioning and Processing Speed; FSS = Fatigue Severity Scale; GDS = Geriatric
Depression Scale; O/M = Orientation and Memory; VAF = Visual Analog Fatigue; VCI = vascular cognitive impairment.
Excluding age and sex, all p values are adjusted for age and sex. Only those with cysteine-changing variants were included.
a p < 0.05.
b p < 0.01.
c Indicates missing data, in eTable 1.

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Table 2 Comparison of Demographics, Clinical History, Cognitive Behavioral Symptoms, and MRI Parameters Between
Those With and Without Fatigue on the FSS

FSS fatigue

No (n = 84) Yes (n = 90) Comparison

Demographics and vascular risk factors

Age at BMET (mean, SD, min-max) 49.73, 12.42, 22–71 52.88, 12.49, 22–77 p = 0.09

Sex (% male) 38 (45.24) 32 (64.44) p = 0.25

EGFr grouping (median, IQR, min-max) 4, 8.25, 1–31 4, 3, 2–31 p = 0.15

EGFr grouping 1–6 (%) 56 (66.67) 68 (75.56) p = 0.11

History of hypertension (% yes) 19 (22.62) 25 (27.78) p = 0.57

Diabetes mellitus (% yes) 3 (3.57) 8 (8.89) p = 0.16

History of ever smoking (% yes) 28 (33.33) 29 (32.22) p = 0.79

Medical/clinical history

History of migraine (% yes) 61 (72.62) 67 (74.44) p = 0.90

History of migraine with aura (% yes) 58 (69.05) 62 (68.89) p = 0.81

History of TIA (% yes) 8 (9.52) 6 (6.67) p = 0.20

History of stroke (% yes) 30 (35.7) 36 (40.0) p = 0.84

Recurrent stroke (% yes) 4 (4.76) 10 (11.11) p = 0.24

History of encephalopathy (% yes) 4 (4.76) 8 (8.89) p = 0.40

History of epilepsy (% yes) 10 (11.90) 8 (8.89) p = 0.54

Behavioral symptomsc

History of psychiatric disorder (any (%
yes))

21 (25.0) 43 (48.31) p = 0.003b

History of depression (% yes) 16 (19.05) 32 (35.56) p = 0.02a

GDS total score (median, IQR, min-
max)

5, 6.5, 0–30 12, 8, 0–29 p < 0.001b

GDS apathy (median, IQR, min-max) 1, 3, 0–10 3, 5, 0–10 p < 0.001b

GDS anxiety (median, IQR, min-max) 1, 3, 0–10 3, 4, 0–10 p < 0.001b

GDS fatigue (median, IQR, min-max) 2, 3, 0–8 5, 3, 0–9 p < 0.001b

Cognitive symptomsc

History of cognitive impairment (%
yes)

8 (9.52) 29 (32.22) p = 0.001b

BMET total score (median, IQR, min-
max)

15, 3, 7–16 13, 4, 2–16 p = 0.001b

EF/PS score (median, IQR, min-max) 8, 1, 0–8 7, 2, 0–8 p = 0.03a

O/M score (median, IQR, min-max) 7.5, 2, 3–8 6, 3, 0–8 p = 0.001b

VCI on BMET (% yes) 22 (26.83) 45 (51.72) p = 0.001b

Neuroimaging (mean, SD, min-max)c

Total brain volume (mm3) 1,605,907.48, 146,626.35,
1,116,760.97–1,905,869.62

1,587,384.69, 157,759.07,
1,050,256.91–1962324.50

p = 0.81

White matter volume (mm3) 839,916.35, 119,901.36,
287,863.70–1130497.80

811,109.66, 155,801.54,
208,052.97–1,031,950.31

p = 0.32

Gray matter volume (mm3) 765,740.41, 91,976.03, 498,596.65–998,105.20 776,276.27, 140,284.97,
500,548.92–1,319,400.45

p = 0.34

Continued

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and cognitive impairment, with depression being the stron-
gest predictor of fatigue. Path analysis indicated that the re-
lationship between depression and fatigue was bidirectional.

The prevalence of fatigue in this cohort is similar to esti-
mates for poststroke fatigue of around 50%.3,4 Despite its
high prevalence, there is little to no research establishing
the prevalence, and predictors, of fatigue in CADASIL
cohorts; our findings emphasize the importance of this
symptom in this patient group. Studies have repeatedly
shown the major impact of fatigue on patient quality of life
in stroke.33,34

It has been suggested that disease severity, and particularly the
presence of stroke, may be associated with fatigue. However,
while CADASIL itself was associated with increased fatigue,
we found no association between fatigue and brain markers of
disease severity within the CADASIL cohort. In particular,
there was no association with a history of stroke or with lacune
count on MRI. We also found no association with WMH
volume. Some previous studies have reported an association
between WMH severity and fatigue in SVD7; however, a re-
cent systemic review found no robust evidence supporting
this association and found few correlations between neuro-
imaging features and the presence of poststroke fatigue.35

We found a robust association between depression and fa-
tigue within the CADASIL cohort. This was seen both when
binarized scores were used (logistic regression and media-
tion analyses) and when continuous scores were used (path
analysis). Depression has been frequently reported in those with
fatigue, and the 2 symptoms are known to be comorbid.1,13

Likewise, research has found symptoms within depression
and fatigue to be highly correlated, with overlap in domains such
as insomnia and poorer concentration.36 Our results suggest that
the relationship between fatigue and depression is bi-
directional, further supporting research highlighting that
comorbid depression and fatigue may exacerbate one an-
other and worsen outcomes.37 Such findings raise the pos-
sibility that the fatigue associated with SVD is not caused
directly by the disease process, but in an indirect effect of
a syndrome including coexisting fatigue and depression. It
follows that, in theory, treatment of depression should al-
leviate symptoms of fatigue by reducing the effects of the
bidirectional relationship. However, a recent review con-
cluded that antidepressants had limited impact on reducing
fatigue.38 Recent research has suggested that the mechanism
of fatigue may be heterogeneous with a number of different
mechanisms underlying fatigue1,5; therefore, it is possible
that depression may play a role in only some cases; more
accurate characterization of the different fatigue subtypes
and selection of those in whom depression plays a role may
allow better treatment responses. In addition, the use of

Table 2 Comparison of Demographics, Clinical History, Cognitive Behavioral Symptoms, and MRI Parameters Between
Those With and Without Fatigue on the FSS (continued)

FSS fatigue

No (n = 84) Yes (n = 90) Comparison

WMH volume (mm3) 80,837.17, 66,659.24, 0–356561.15 88,366.52, 65,193.32, 0–277447.07 p = 0.70

CMB count 3.97, 12.04, 0–80 10.47, 35.55, 0–197 p = 0.48

Lacune count 2.41, 3.57, 0–18 3.15, 4.20, 0–19 p = 0.47

Abbreviations: BMET = Brief Memory and Executive Test; CMB = cerebral microbleed; EF/PS = Executive Functioning and Processing Speed; EGFr = epidermal
growth factor-like repeat; GDS = Geriatric Depression Scale; O/M = Orientation and Memory; VCI = vascular cognitive impairment; WMH = white matter
hyperintensity.
Excluding age and sex, all p values are adjusted for age and sex. All brain volumes and WMH volume were normalized to account for skull size.
a p < 0.05.
b p < 0.01.
c Indicates missing data, in eTable 1.

Figure 1 Venn Diagram Showing Comorbidities Between
Cognitive Impairment on BMET, Depression on
the GDS, and Fatigue on the FSS in the CADASIL
Cohort

BMET = Brief Memory and Executive Test; FSS = Fatigue Severity Scale;
GDS = Geriatric Depression Scale.

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fatigue management may play a role in alleviating the
symptoms associated with SVD.

Our results also showed that fatigue was increased in those
patients with CADASIL with cognitive impairment. Assess-
ment of cognitive subdomains showed that the EF/PS score
was more closely linked to depression than to fatigue. It has
been hypothesized that reduced executive functioning and
processing speed may be part of presentation in depression.39

Similarly, both EF/PS and depression have been associated
with the extent of white matter damage in SVD.40,41 As such, it
follows that these 2 variables would be most closely associated
in these analyses. Initial analyses of the group characteristics
showed that more patients showed specific impairments in
EF/PS; however, O/M mediation analyses indicated that this
variable was more associated with fatigue. It can be speculated
that, in this instance, increased fatigue reduced attentional

resources required for memory encoding and retrieval pro-
cesses, withmemory impairment also common in depression.42

Furthermultivariate analyses differentiating fatigue interactions
with different facets of cognition are needed to explore this
further in this and other conditions.

Our study has several strengths. It included a large cohort of
patients with CADASIL who were prospectively recruited. We
collected measures on fatigue, depression, and cognition
allowing the inter-relationships between the 3 to be de-
termined. However, it also has limitations. We used clinically
acquired MRI scans; therefore, they were not uniform in make
of scanner or type of sequences used. We only used one
measure of fatigue, the FSS. This has been well validated and
widely used in neurologic populations and has been shown to
have a reliable single-factor measurement.43 However, it is now
recognized that fatigue is highlymultidimensional and has been

Figure 2Direct andTotal Effects of GDSDepression (A) andBMETVCI (Cognition) (B) on FFS Fatigue in theMediationModels

c represents the effect of one variable on another (total ef-
fect), whereas c’ represents this effect while further re-
moving the effects of the third mediated variable (direct
effect). BMET = Brief Memory and Executive Test; FSS = Fa-
tigue Severity Scale; VCI = vascular cognitive impairment.

Figure 3 Path Diagram of Relationships Among Cognition, Depression, and Fatigue

*p < 0.05, **p < 0.01. Rectangles denote variables included in
the path model. Single-headed arrows represent hypothe-
sized causal pathways, with adjacent numbers indicating
standardized regression coefficients. Double-headed arrows
represent covariance between variables. e1, e2, and e3 de-
note the error terms or residuals for each endogenous vari-
able (GDS score, average FSS score, and BMET total score),
representing the variance unexplained by the model’s pre-
dictors. BMET = Brief Memory and Executive Test; FSS = Fa-
tigue Severity Scale; GDS = Geriatric Depression Scale.

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Table 3 Direct, Indirect, and Total Effects Estimated Using the Path Model

Endogenous variables Predictors

Estimate of direct effect

p Value

Estimate of indirect effect

p Value

Estimate of total effect

p ValueUnstandardized Standardized Unstandardized Standardized Unstandardized Standardized

GDS score Average FSS score 2.326 0.549 0.004a NA NA NA 2.326 0.549 0.004a

BMET total score NA NA NA NA NA NA NA NA NA

Sex 1.513 0.103 0.186 NA NA NA NA NA NA

Average FSS score GDS score 0.186 0.790 0.054a NA NA NA 0.186 0.790 0.054a

BMET total score NA NA NA NA NA NA NA NA NA

MRS score 0.415 0.214 0.068 NA NA NA NA NA NA

BMET total score Average FSS score −2.335 −1.348 0.152 0.978 0.594 0.489 −1.358 −0.783 0.003a

GDS score −0.010 −0.040 0.588 −0.435 −1.064 0.361 −0.015 −0.036 0.930

Age 0.420 1.028 0.436 NA NA NA NA NA NA

Abbreviations: BMET = Brief Memory and Executive Test; FSS = Fatigue Severity Scale; GDS = Geriatric Depression Scale.
Note: n = 153. NA denotes the path not modeled. Where the direct path was the only path modeled, this is also the total effect. Standardized coefficients (mean = 0, SD = 1) allow for comparison across variables while
unstandardized coefficients retain the original measurement scale of each variable.
a Significant path.

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associated with a multitude of factors.1 Likewise, recent re-
search has suggested that fatigue measures may measure sep-
arate aspects of fatigue.43 Further investigation with
a multidimensional fatigue scale, or indeed, battery of fatigue
questionnaires, may better encapsulate the different facets of
fatigue.

In summary, we have shown a high prevalence of fatigue in
patients with CADASIL. Depression was the major risk factor
of fatigue, and path analysis showed a bidirectional relation-
ship between fatigue and depression, suggesting that at least in
some cases of fatigue, targeting depression may improve
outcome. By contrast, we found no association of clinical
features and the extent of white matter damage on MRI with
fatigue.

Author Contributions
A.A. Jolly: drafting/revision of the manuscript for content,
including medical writing for content; major role in the ac-
quisition of data; study concept or design; analysis or in-
terpretation of data. S. Anyanwu: drafting/revision of the
manuscript for content, including medical writing for content;
major role in the acquisition of data. F. Koohi: drafting/
revision of the manuscript for content, including medical
writing for content; analysis or interpretation of data. R.G.
Morris: drafting/revision of the manuscript for content, in-
cluding medical writing for content; analysis or interpretation
of data. H.S. Markus: drafting/revision of the manuscript for
content, including medical writing for content; study concept
or design; analysis or interpretation of data.

Acknowledgment
The authors thank all patients with CADASILwho participated
in the UK Familial Study and the recruiting centers. UK
Familial Recruiting Centers (hospital, local investigators
[number recruited by site]): Cambridge University Hospitals
NHS Foundation Trust: Markus H. S., Edwards H., Henthorn
M., and Jolly A. (294); Leeds Teaching Hospital NHS Trust:
Hassan A., FarmanM., andWaugh D. (30); Royal Hallamshire
Hospital Sheffield: Harkness K., Kamara C., Richards E., and
Howe J. (20); St George’s Healthcare NHS Trust, London:
Khan U., Ghalata R., Williams R., and Zilahi B. (8); University
College London Hospitals NHS Foundation Trust: Werring
D., Lee YC., Marinescu M., O’Barey S., Raingold G., and
Scheherazade F. (7).

Study Funding
UK Familial Cerebral Small Vessel Disease is funded by the
British Heart Foundation Programme Grant (RG/4/32218).
This research was supported by the NIHR Cambridge Bio-
medical Research Center (BRC-1215-20014) and the Cam-
bridge BHF Center of Research Excellence [RE/18/1/
34212]. Recruitment was supported by the NIHR Clinical
Research Network. FK was supported by Dementia Platforms
UK (RRAG 398MRCDPUK). The views expressed are those
of the author(s) and not necessarily those of the NIHR or the
Department of Health and Social Care.

Disclosure
The authors report no relevant disclosures. Go to
Neurology.org/N for full disclosures.

Publication History
Received by Neurology July 24, 2024. Accepted in final form
December 4, 2024. Submitted and externally peer reviewed. The
handling editor was Editor-in-Chief José Merino, MD, MPhil, FAAN.

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