Pain and depression are associated with both physical and mental fatigue independently of comorbidities and medications in primary Sjögren's syndrome

Abstract

Objectives
To report on fatigue in patients from the United Kingdom Primary Sjögren’s Syndrome Registry identifying factors associated with fatigue and robust to assignable causes such as comorbidities and medications associated with drowsiness.

Methods
From our cohort (n=608) we identified those with comorbidities associated with fatigue, and those taking medications associated with drowsiness. We constructed dummy variables, permitting the contribution of these potentially assignable causes of fatigue to be assessed. Using multiple regression analysis, we modelled the relationship between Profile of Fatigue and Discomfort (PROFAD) physical and mental fatigue scores and potentially related variables.

Results
Pain, depression, and daytime sleepiness scores were closely associated with both physical and mental fatigue (all p≤0.0001). In addition, dryness was strongly associated with physical fatigue (p≤0.0001). These effects were observed even after adjustment for co-morbidities associated with fatigue or medications associated with drowsiness.

Conclusions
These findings support further research and clinical interventions targeting pain, dryness, depression and sleep to improve fatigue in PSS patients.

Full text

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Hackett KL, etal. RMD Open 2019;5:e000885. doi:10.1136/rmdopen-2018-000885
ORIGINAL ARTICLE
Pain and depression are associated with
both physical and mental fatigue
independently of comorbidities and
medications in primary
Sjögren’s syndrome
Kate L Hackett,1 Kristen Davies,2 Jessica Tarn,3 Rebecca Bragg,3 Ben Hargreaves,3
Samira Miyamoto,3,4 Peter McMeekin,1 Sheryl Mitchell,5 Simon Bowman,6
Elizabeth J Price,7 Colin Pease,8 Paul Emery,9,10 Jacqueline Andrews,10
Peter Lanyon, 11 John Hunter,12 Monica Gupta,12 Michele Bombardieri,13
Nurhan Sutcliffe,14,15 Costantino Pitzalis,16 John McLaren,17 Annie Cooper,18
Marian Regan,19 Ian Giles,20 David Isenberg,20 Saravan Vadivelu,21 David Coady,22
Bhaskar Dasgupta,23 Neil McHugh,24 Steven Young-Min,25 Robert Moots, 26
Nagui Gendi,27 Mohammed Akil,28 Bridget Grifths,29 Dennis W Lendrem, 3,30
Wan-Fai Ng,30,31 On behalf of the UK primary Sjögren’s Syndrome Registry
To cite: HackettKL, DaviesK,
TarnJ, etal. Pain and
depression are associated with
both physical and mental fatigue
independently of comorbidities
and medications in primary
Sjögren’s syndrome. RMD Open
2019;5:e000885. doi:10.1136/
rmdopen-2018-000885
►Prepublication history and
additional material for this
paper are available online. To
view these les, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ rmdopen- 2018-
000885).
KLH and KD are joint rst
authors.
Received 18 December 2018
Revised 20 March 2019
Accepted 27 March 2019
For numbered afliations see
end of article.
Correspondence to
Dr Wan-Fai Ng;
wan- fai. ng@ newcastle. ac. uk
Sjögren syndrome
© Author(s) (or their
employer(s)) 2019. Re-use
permitted under CC BY-NC. No
commercial re-use. See rights
and permissions. Published
by BMJ.
ABSTRACT
Objectives To report on fatigue in patients from the
United Kingdom primary Sjögren’s syndrome (pSS) registry
identifying factors associated with fatigue and robust to
assignable causes such as comorbidities and medications
associated with drowsiness.
Methods From our cohort (n = 608), we identied those
with comorbidities associated with fatigue, and those
taking medications associated with drowsiness. We
constructed dummy variables, permitting the contribution
of these potentially assignable causes of fatigue to be
assessed. Using multiple regression analysis, we modelled
the relationship between Prole of Fatigue and Discomfort
physical and mental fatigue scores and potentially related
variables.
Results Pain, depression and daytime sleepiness
scores were closely associated with both physical and
mental fatigue (all p ≤ 0.0001). In addition, dryness was
strongly associated with physical fatigue (p ≤ 0.0001).
These effects were observed even after adjustment for
comorbidities associated with fatigue or medications
associated with drowsiness.
Conclusions These ndings support further research and
clinical interventions targeting pain, dryness, depression
and sleep to improve fatigue in patients with pSS.
This nding is robust to both the effect of other
comorbidities associated with fatigue and medications
associated with drowsiness.
INTRODUCTION
Primary Sjögren’s syndrome (pSS) is a
chronic, systemic autoimmune disease
characterised by functional impairment of
the lachrymal and salivary glands. The disease
can result in systemic involvement, which
Key messages
What is already known about this subject?
►Patients with primary Sjögren’s syndrome (pSS) of-
ten report fatigue as the symptom most in need of
improvement.
What does this study add?
►This study identies pain, depression and daytime
sleepiness scores as those factors most closely as-
sociated with both physical and mental fatigue in
pSS.
►This nding is robust to both the effect of other co-
morbidities associated with fatigue and medications
associated with drowsiness.
How might this impact on clinical practice?
►Interventions directed at managing fatigue might
be expected to have a signicant impact on the pa-
tient’s quality of life.
►These ndings support an interdisciplinary clinical
assessment with:
– an initial medication review,
– assessment for comorbidities associated with
fatigue,
– screening for anxiety and depression,
– assessment for undiagnosed sleep disorders,
– pain management interventions and
– interventions targeting fatigue management
directly.
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may affect any organ, resulting in manifestations such
as vasculitis, neuropathy, as well as skin, lung and kidney
involvement.1 Patients with pSS often report fatigue as an
important symptom in need of management.2 Defined as
an overwhelming sense of tiredness, lack of energy and a feeling
of exhaustion,3 4 fatigue is associated with poor health5 and
functional impairment.6 Previous studies have shown that
physical fatigue measured with the somatic component
of the Profile of Fatigue scale (PROFAD)7 is more preva-
lent and severe in patients with pSS than mental fatigue
as measured with the mental component of the same
scale.7–9 Unsurprisingly, patients with pSS also report
higher daytime sleepiness scores.9
For some patients with pSS, fatigue is likely to have an
assignable cause other than pSS. For example, they may
be taking medications associated with drowsiness, or have
comorbidities associated with fatigue—including hypo-
thyroidism, depression, obesity, coeliac disease, diabetes
and anaemia.
While there have been several previous studies exam-
ining the relationship between pSS, fatigue and other
factors, these were conducted with lower numbers of
patients and without regard to confounding medications
or comorbidities. Furthermore, few studies have used the
PROFAD which is a fatigue measure developed specifi-
cally for pSS.7
The objective of this paper is to report on the fatigue
symptoms of a large cohort of 608 patients diagnosed with
pSS defined using the American European Consensus
Group (AECG) classification criteria and to identify
factors associated with fatigue, and robust to assignable
causes such as comorbidities and medications associated
with fatigue.
METHODS
Design
The United Kingdom primary Sjögren’s syndrome
registry (UKPSSR, www. sjogrensregistry. org) is a proto-
col-driven, multicentre, cross-sectional study using stand-
ardised patient-reported measures and physician-gen-
erated assessments of patients with pSS from across the
UK.10 All patients are participants in the UKPSSR and
fulfil the AECG classification criteria. Note that fibromy-
algia was an explicit exclusion criterion for the UKPSSR.
Informed consent was obtained from all participants
according to the principles of the Helsinki Declaration.
Research ethics approval was granted by the North West
Research Ethics Committee in the UK. All clinical and
laboratory data were collected prospectively using a stand-
ardised pro-forma at the time of recruitment as previously
described.10 At the time of analysis, 639 patients had been
recruited to UKPSSR and complete data were available
for 608 patients.
Fatigue assessment
The Profile of Fatigue and Discomfort (PROFAD)7 was
designed specifically for patients with pSS. It includes
six questions which assess somatic and mental fatigue
on an eight-point (0–7) scale. An average is taken for
each domain score with a score of above 2.0 and 1.8
being considered significant for the somatic fatigue and
mental fatigue domains, respectively.7 8 The PROFAD
also includes a 10 cm visual analogue score of fatigue,
which provides a score of 0 for absent to 100 for worst
imaginable perceived fatigue levels.
Pain, depression and other assessments of patients with pSS
The following patient-reported outcome measures are
collected for all UKPSSR pSS subjects: Epworth Sleepi-
ness Scale (EPWORTH), Hospital Anxiety and Depres-
sion Scale (HADs—anxiety and depression), Euro-
pean League Against Rheumatism (EULAR) Sjögren's
syndrome Patient Reported Index (ESSPRI) (measure
of overall symptom burden and includes a 1–10 pain
score), EULAR Sicca Score (ESS) and EULAR Sjögren’s
Syndrome Disease Activity Index (ESSDAI).
All UKPSSR participants are asked to indicate which
symptom they perceive as being the most in need of
improvement (physical fatigue, mental fatigue, dryness
or pain). At the time of recruitment, the presence of
anti-Ro and/or anti-La antibodies are recorded, as well
as levels of systemic inflammation including C-reactive
protein and erythrocyte sedimentation rate. Additionally,
body mass index (BMI) and co-morbidities and medica-
tions are recorded for each participant.
We captured the effect of multiple drugs and comorbid-
ities using a comorbidity and polypharmacy score (CPS).
This score is obtained by combining the number of comor-
bidities and the number of prescribed medications.11 To
identify potentially assignable causes for fatigue in patients
with pSS, we analysed our cohort for existing comorbidities
and medication status. We classified the following comor-
bidities as conditions potentially associated with fatigue
independently of pSS: hypothyroidism, diabetes mellitus
(insulin-dependent diabetes mellitus (IDDM) and non-in-
sulin-dependent diabetes mellitus (NIDDM)), coeliac
disease, anaemia (haemoglobin <10), a diagnosis of clinical
depression and severe obesity (BMI ≥40).12 13 In addition,
patients whose fatigue might be assignable to medications
linked with drowsiness (eg, antidepressants, antipsychotics,
opioids, etc) were identified—see online supplementary
table S1. Using these data, we created dummy variables for
each patient to adjust for medications associated with drows-
iness (DROWSY Medications) or comorbidities associated
with fatigue (COMORBID).
Statistical analysis
Multiple regression analysis was used to model the relation-
ships between physical and mental fatigue and candidate
variables, including age, sex, pain, dryness, anxiety, depres-
sion, daytime sleepiness scores, BMI, symptom years, drowsy
medications (DROWSY) and comorbidities (Anaemia
COMORBID, Hypothyroidism COMORBID, IDDM
COMORBID, NIDDM COMORBID, Obesity COMORBID,
Depression COMORBID, Coeliac COMORBID). Causation
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Sjögren syndromeSjögren syndromeSjögren syndrome
Table 1 Numbers of patients presenting with
comorbidities. While 170 patients presented with one and
22 presented with two or more comorbidities, most patients
(416) presented with no comorbidities
Comorbidities
Number of
patients
None 416
THYROID 114
COELIAC 16
DEPRESSION 16
DIABETES 10
OBESITY 8
THYROID+OBESITY 8
ANAEMIA 6
THYROID+DEPRESSION 3
THYROID+DIABETES 3
DIABETES+OBESITY 2
THYROID+ANAEMIA 2
DIABETES+DEPRESSION 1
ANAEMIA+COELIAC 1
THYROID+COELIAC 1
THYROID+DIABETES+OBESITY 1
Total 608
COMORBIDITIES Key: THYROID=hypothyroidism,
COELIAC=coeliac disease, DEPRESSION=clinical
depression diagnosis, DIABETES=IDDM or NIDDM diabetes,
OBESITY=obesity, ANAEMIA=anaemia.
cannot be inferred from such models: they can only hope to
highlight those variables associated with fatigue after adjust-
ment for the presence of other covariates. The assumptions
underlying the multiple regression analysis were tested by
lack-of-fit testing and inspection of residuals. Statistical anal-
yses were performed using SAS JMP (V.13) Statistical Data
Visualisation Software. Statistical significance was set at a p
value of ≤0.05.
RESULTS
The median age of our cohort (n=608) was 64 years
(IQR: 53–71). The median number of symptom years for
patients was 10 years (IQR: 5–17). Most UKPSSR patients
experienced dryness as measured by objective measures
of dryness such as Salivary Flow and Schirmer’s Test.
The median score on the ESSPRI scale was 5.67 (IQR:
3.67–7.00) and the EULAR Dryness Scale (ESS) was 6
(IQR: 4–8). The median disease activity score (ESSDAI)
for the cohort was 3 (IQR: 1–7). While more than half
of patients received one or more medications to manage
their dryness symptoms, 40% of patients ranked fatigue
or mental fatigue—rather than dryness—as the symptom
‘most in need of improvement’. This is similar to the
numbers ranking dryness as the most in need of improve-
ment (45%), and rather more than those ranking pain as
the symptom most in need of improvement (15%).
Comorbidities and medications associated with drowsiness
In addition to meeting AECG classification criteria for
Sjogren’s syndrome, patients in the UKPSSR cohort are
an older cohort presenting with relatively complex health
needs. The median CPS for the cohort was 6 (IQR: 3–9).
Almost one-third of patients with pSS presented with
one or more fatigue-related comorbidities. The most
commonly reported comorbidity was hypothyroidism,
see table 1.
In addition, 130 patients with pSS were prescribed a
number of different drugs associated with drowsiness.
The most common drowsiness-associated medications
included antidepressants, such as citalopram (18%),
amitriptyline (12%) and fluoxetine (11%), or analgesics
such as tramadol (13%). Some patients took two or more
drowsiness-associated medications, see table 2.
For information, summary statistics are reported sepa-
rately for four separate subsets of patients: (a) patients
with comorbidities associated with drowsiness, (b)
patients with medications associated with drowsiness, (c)
patients with both comorbidities and medications associ-
ated with drowsiness and (d) patients with neither comor-
bidities nor medications associated with drowsiness, see
online supplementary table S2. Note that most patients
presented with neither comorbidities nor medications
associated with drowsiness. While apparent differences
between subsets must be interpreted with caution—see
online supplementary appendix—not surprisingly, there
were statistically significant differences in both physical
and mental PROFAD fatigue scores for patients with
comorbidities associated with fatigue or medications asso-
ciated with drowsiness (Kruskal-Wallis test, p<0.0001).
Models of fatigue
In order to identify variables associated with physical and
mental fatigue, we ran multiple regression models sepa-
rately for PROFAD physical and PROFAD mental fatigue
scores. The models included the candidate variables: age,
sex, symptom years, anti-Ro or anti-La positivity, pain,
dryness, Epworth Sleepiness Scale and BMI. In addition,
we included dummy variables for comorbidities associ-
ated with drowsiness—anaemia, obesity, hypothyroidism,
IDDM, NIDDM, coeliac disease and a diagnosis of clinical
depression (labelled respectively, Anaemia COMORBID,
Obesity COMORBID, Hypothyroidism COMORBID, IDD
COMORBID, NIDD COMORBID, Coeliac COMORBID and
Depression COMORBID). The dummy variable DROWSY
Medications captured those patients on medications associ-
ated with drowsiness. A list of medications associated with
drowsiness is given in the online supplementary appendix.
In addition, the model included anxiety and depression
scores from the HADS. Given the large number of candi-
date variables, we used a Benjamini-Hochberg correction to
adjust the false discovery rate (p value=0.05).14 Further statis-
tical details are given in the online supplementary file. Full
multiple regression equations for PROFAD physical and
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Table 2 Numbers of patients prescribed drowsy
medications. While 108 patients took one and 22 took two
or more drowsy medications, most patients (478) took no
drowsy medications. Of those taking one or more drowsy
medications, citalopram, tramadol, amitriptyline, uoxetine
and zopiclone were the most frequently reported
Drowsy medications
Number of
patients
None 478
CITALOPRAM 23
TRAMADOL 17
AMITRIPTYLINE 16
FLUOXETINE 14
ZOPICLONE 4
DIAZEPAM 3
DOSULEPIN 3
GABAPENTIN 3
PAROXETINE 3
BACLOFEN 2
DULOXETINE 2
IMIPRAMINE 2
MIRTAZAPINE 2
SERTRALINE 2
TEMAZEPAM 2
ZOLPIDEM 2
CARBAMAZEPINE 1
CLOMIPRAMINE 1
CLONAZEPAM 1
LAMOTRIGINE 1
LEVETIRACETAM 1
NORTRIPTYLINE 1
RISPERIDONE 1
SODIUM VALPROATE 1
CARBAMAZAPINE ESCITALOPRAM AMITRIPTYLINE 1
DIAZEPAM DOSULEPIN 1
DULOXETINE AMITRIPTYLINE CITALOPRAM 1
ESCITALOPRAM DIAZEPAM 1
GABAPENTIN FLUOXETINE 1
LAMOTRIGINE CITALOPRAM 1
TOPIRAMATE MIRTAZAPINE AMITRIPTYLINE 1
TRAMADOL AMITRIPTYLINE 1
TRAMADOL CITALOPRAM 3
TRAMADOL DOSULEPIN 1
TRAMADOL FLUOXETINE DIAZEPAM 1
TRAMADOL GABAPENTIN 1
TRAMADOL SERTRALINE LITHIUM CHLORPROMAZINE
TEMAZEPAM
1
TRAMADOL ZOPICLONE 1
VENLAFAXINE ROPINIROLE QUETIAPINE 1
ZOLPIDEM SERTRALINE 1
ZOPICLONE AMITRIPTYLINE 1
ZOPICLONE FLUOXETINE 1
ZOPICLONE LAMOTRIGINE FLUOXETINE 1
ZOPICLONE OLANZAPINE GABAPENTIN DIAZEPAM 1
Total 608
Figure 1 Summary multiple regression charts for (A)
PROFAD physical fatigue and (B) PROFAD mental fatigue
scores. Log worth values indicate the relative importance
of each variable in the model. The broken, blue, vertical
reference line is for Log worth=2.0 equivalent to an false
discovery rate-adjusted p value of 0.01. Note that both pain
and depression are closely associated with both physical and
mental fatigue. See text. BMI, body mass index; PROFAD,
Prole of Fatigue and Discomfort.
PROFAD mental scores are presented in online supplemen-
tary table 3. These analyses are summarised in figure 1. Note
that while there was a weak association between ESSDAI
disease activity scores and PROFAD fatigue scores, this was
not robust to the inclusion of other covariates, see online
supplementary appendix.
Interestingly, pain and depression scores are correlated
with both PROFAD physical fatigue and PROFAD mental
fatigue scores. The physical fatigue model identified pain,
depression, dryness and scores on the Epworth daytime
sleepiness scale as the most important variables associ-
ated with PROFAD physical fatigue scores (all p≤0.0001).
The mental fatigue model identified pain, depression
and scores on the Epworth daytime sleepiness scale as the
three most important variables associated with PROFAD
mental fatigue scores (all p≤0.0001).
The strong association of depression on the HADS
with both physical and mental fatigue prompted us to
repeat the analysis with depression COMORBID recoded
to include a new category—those patients with a HADS
score >8 or a depression score >8. Such patients warrant
further assessment for depression and may represent
a subset of those patients with currently undiagnosed
depression. Repeating the analysis, even after adjustment
for potential undiagnosed depression, the relationships
of pain, depression, dryness and Epworth daytime sleep-
iness scores remained statistically significant (p<0.0001).
None of the comorbidities associated with fatigue had
a statistically significant relationship with either PROFAD
physical or PROFAD mental scores. However, patients
prescribed medications associated with drowsiness had
higher PROFAD physical fatigue scores (p=0.0001).
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Sjögren syndromeSjögren syndromeSjögren syndrome
DISCUSSION
Fatigue is important to patients with pSS
This is the largest study to date investigating fatigue in
patients with pSS. Our study confirms previous observations
that fatigue is an important symptom in need of improve-
ment.15 The main objective was to report on fatigue in
patients with pSS after separating out effects attributable to
assignable causes such as comorbidities and medications.
Our data suggest that while patients with pSS may present
with complex comorbidities and medications associated
with fatigue, these account for a relatively small proportion
of the variation in fatigue symptoms.
Pain, depression and dryness
Fatigue symptoms are important to patients with pSS and
there is an association between fatigue and other symp-
toms such as pain and depression.16 17 Howard Tripp et
al18 identified pain and depression scores, in association
with the pro-inflammatory cytokines inducible protein
10 and interferon gamma, as the two symptoms best
predicting fatigue groups.18
Pain is associated with both mental and physical
fatigue.19 The origin of pain in pSS is unclear, although
research points towards neuropathic pain being the
most common type of pain in pSS, followed by nocicep-
tive pain.16 The relationship between pain and fatigue
is reported in other conditions including rheumatoid
arthritis (RA), systemic lupus erythematosus and anky-
losing spondylitis (AS).20–22 Previous research using
anti-tumour necrosis factor inhibitors in RA suggests that
the reductions in reported fatigue were attributable to a
reduction in pain, rather than direct effects on fatigue
itself23 and pain may be a mediator of fatigue. Pain can be
modified through medications which have an association
with drowsiness such as amitriptyline and gabapentin.
This contribution gives a possible explanation as to why
drowsy medications do not have a major role in predicting
mental fatigue.
Interestingly, we found that a diagnosis of clinical depres-
sion was not associated with higher fatigue. It is possible
that those patients with a pre-existing depression diagnosis
have been successfully treated for their depression and
consequently are less likely to experience fatigue. However,
less than 5% of our cohort had a clinical diagnosis of
depression—rather less than that for other rheumatic
diseases24—and yet more than half presented with HADS
scores suggesting they be screened for depression or were
treated with antidepressants. If depression were under-re-
ported, then this might lead to an apparent association
between depression scores and fatigue. However, the rela-
tionship between depression scores and fatigue was robust
to the introduction of an additional classification capturing
those patients with a potentially undiagnosed depression.
Dryness is one of the characteristic symptoms of pSS
and we observed a strong relationship between dryness
symptoms and physical fatigue. Dryness may be directly
related to fatigue, or it may act as an indicator of either
severity or disease activity. In a recent qualitative study
of pSS fatigue, patients described experiencing ‘ocular
fatigue’, where they experienced tiredness in their eyes,
which for many, related to their ocular dryness symp-
toms such as feeling gritty or sore.25 High dryness scores
may simply indicate that the disease is highly active, or
needs to be more closely managed in the clinic. Dryness
symptoms may be an indicator of autonomic dysfunction,
and fatigue may be a result of autonomic dysfunction.
In addition, dryness symptoms may be associated with
changes in nocturnal behaviour—see below.
Sleep
The Epworth Sleepiness Scale can be used as a screening
tool to identify patients who potentially have obstructive
sleep apnoea and other primary sleep disorders.26 We
observed that the scores on the Epworth scale were associ-
ated both with physical and mental fatigue. This finding is
similar to a recent study in RA, which found that both phys-
ical and mental fatigue were associated with poor sleep.27
While there are limitations to the Epworth scale, this does
suggest that patients with pSS be screened for sleep prob-
lems. This could include incorporating information from a
caregiver, partner or others, using other sleep instruments,
polysomnography and other tests with referral to special-
ists in sleep disorders. A recent review has identified that a
range of sleep disturbances, including night awakenings, are
common in patients with pSS.28 Troublesome sicca symp-
toms, pain and nocturia have been identified as symptoms
which can potentially disturb sleep causing a reduction in
sleep quality.28 Identification of specific sleep disturbances
in this patient group is essential, and a combination of objec-
tive and subjective measures is required to identify specific
primary sleep disorders,29 30 thereby ensuring that patients
have access to appropriate interventions. Treatment of sleep
disturbances in other rheumatic diseases has resulted in a
reduction of pain, fatigue and depression.31
RECOMMENDATIONS
Our data confirm the importance of fatigue symptoms—
both mental and physical—to patients with pSS and
permit identification of factors contributing to fatigue
including other comorbidities and medications associ-
ated with drowsiness. Given the wide variety of potential
factors contributing to fatigue, we support the view that
a multidisciplinary approach is essential for the clinical
management of fatigue in pSS.32
►We observe that many patients with pSS are taking
multiple medications and we recommend a medica-
tion review be undertaken to identify drowsy medica-
tions which could be contributing to fatigue. If these
medications are discontinued, then a review should
be arranged and if the fatigue does not improve,
then treatment of comorbidities should be consid-
ered. However, as pain is a major contributor of both
mental and physical fatigue, the contribution of
some pain-modifying medications may be beneficial,
despite their association with drowsiness.
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►Patients with pSS should be tested for common comor-
bidities which can contribute to fatigue, including
anaemia or hypothyroidism and offered appropriate
treatments.
►We recommend screening for depression and anxiety
and offering patients appropriate interventions to
address these symptoms. Non-pharmacological inter-
ventions (such as talking therapies) may reduce the
need for antidepressants and anti-anxiolytics, many of
which are associated with drowsiness.
►We recommend that patients undergo a more detailed
sleep assessment in order to screen for a primary
sleep disorder. Patients identified with conditions
such as obstructive sleep apnoea should be offered
assignable causes such as Continuous Positive Airway
Pressure (CPAP) treatment.33 Interventions such as
cognitive behavioural therapy for insomnia (CBT-I)
are a first-line treatment for insomnia associated with
other medical conditions34 and may prove beneficial
to patients with pSS.28
►Patients with pSS should be offered appropriate pain
management interventions. If pain is associated with
poor sleep, then CBT-I with a pain adjunct has been
suggested as a feasible treatment.35
►Finally, in the absence of good evidence to support
effective drug treatments, fatigue interventions in
pSS might focus on a multidisciplinary approach
incorporating activity management, graded exercise/
activity and CBT.32
There are limitations to this study. While the PROFAD
system is an established and well-validated tool, there is still
a need for a good objective measure of fatigue. Though a
sample population of 608 patients gives some assurance that
this study is a reasonable cross section of patients in the UK,
it would be useful to have comparative data in the future
from other cohorts. While there was no evidence of biases
arising from missing data, we note that data were incom-
plete for some measures and these are noted in the relevant
tables of summary statistics. In addition, this was an obser-
vational study using cross-sectional data. We cannot infer
causality and are only able to report associations. Prediction
models do not imply causation.
CONCLUSIONS
Our data support that of others in recognising the impor-
tance of fatigue in the clinical management of patients
with pSS.3 5 36–40 Furthermore, our analysis permits the
identification of contributing factors—such as comor-
bidities and drowsiness-associated medications. In addi-
tion, we identified multiple factors associated with both
physical and mental fatigue in pSS. Most notable of these
are pain scores measured on the ESSPRI and depres-
sion scores measured on the HADS. These associations
are robust and observed in patients with pSS even after
adjustment for assignable causes such as other comor-
bidities and use of medications associated with drowsi-
ness. Interventions directed at managing fatigue might
be expected to have a significant impact on the patient’s
quality of life. We recommend that the management of
fatigue in pSS be multidisciplinary, and personalised to
each patient depending on potential contributors to
what is a debilitating and complex symptom.
Author afliations
1Department of Life & Health Sciences, Northumbria University Department of
Public Health and Wellbeing, Newcastle upon Tyne, UK
2Musculoskeletal Research Group, Newcastle University Faculty of Medical
Sciences, Newcastle upon Tyne, UK
3Musculoskeletal Research Group, Newcastle University Faculty of Medical
Sciences, Newcastle upon Tyne, UK
4Departamento de Educação Integrada em Saúde, Universidade Federal do Espirito
Santo, Vitoria, Brazil
5Musculoskeletal Services, Newcastle Upon Tyne Hospitals NHS Foundation Trust,
Newcastle Upon Tyne, UK
6Rheumatology Research Group, University of Birmingham, Birmingham, UK
7Rheumatology, Great Western Hospitals NHS Foundation Trust, Swindon, UK
8Rheumatology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
9Rheumatology, University of Leeds, Leeds Institute of Rheumatic and
Musculoskeletal Medicine, Leeds, UK
10Musculoskeletal, NIHR Leeds Musculoskeletal Biomedical Research Unit, Leeds,
UK
11Rheumatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
12Gartnavel General Hospital, Glasgow, UK
13Experimental Medicine and Rheumatology, Queen Mary University of London,
London, UK
14Barts Health NHS Trust, London, UK
15Rheumatology, Barts and The London School of Medicine and Dentistry, London,
UK
16Experimental Medicine and Rheumatology, William Harvey Research Institute
Experimental Medicine and Musculoskeletal Sciences, London, UK
17NHS Fife, Kirkcaldy, UK
18Royal Hampshire County Hospital, Winchester, UK
19Royal Derby Hospital, Derby, UK
20Centre for Rheumatology, University College London, London, UK
21Queen Elizabeth Hospital, Gateshead, UK
22City Hospitals Sunderland NHS Foundation Trust, Sunderland, UK
23Department of Rheumatology, Southend University Hospital NHS Foundation Trust,
Westcliff-on-Sea, UK
24Department of Pharmacy and Pharmacology, University of Bath, Bath, UK
25Rheumatology, Portsmouth Hospitals NHS Trust, Portsmouth, UK
26Rheumatology, Aintree University Hospitals, Liverpool, UK
27Department of Rheumatology, Basildon Hospital, Basildon, UK
28Department of Rheumatology, Shefeld Teaching Hospitals NHS Foundation Trust,
Shefeld, UK
29Musculoskeletal Services, Newcastle Upon Tyne Hospitals NHS Foundation Trust,
Newcastle Upon Tyne, UK
30Musculoskeletal Ageing, NIHR Newcastle Biomedical Research Centre, Newcastle
upon Tyne, UK
31Musculoskeletal Research Group, Newcastle University Faculty of Medical
Sciences, Newcastle upon Tyne, UK
Acknowledgements We thank all patients and volunteers who joined the UKPSSR
cohort.
Collaborators See Appendix 1 for the full list of members.
Contributors SJB, BG, W-FN conceived the UKPSSR study. Study coordination
and data collection for the UKPSSR was coordinated by SM. Data collection
was performed by SM, SJB, EP, CTP, PE, PL, JH, MG, MB, NS, CP, JMcL, AC, MR,
IG, DI, SV, DC, BD, NMcH, SY-M, RM, NG, MA, BG and W-FN. BH performed data
management and data processing. DWL advised and KLH, KD, JRT, STM and RB
performed the analyses. The paper was drafted by KLH, KD, DWL and W-FN. All
authors reviewed and approved the nal manuscript.
Funding The UKPSSR received grant support from the UK Medical Research
Council (G0800629 to WFN, SJB, BG), British Sjögren’s Syndrome Association
and Newcastle University. This work also received infra-structure support from
the National Institute for Health Research Newcastle Biomedical Research Centre
on 25 April 2019 by guest. Protected by copyright.http://rmdopen.bmj.com/RMD Open: first published as 10.1136/rmdopen-2018-000885 on 24 April 2019. Downloaded from

7
Hackett KL, etal. RMD Open 2019;5:e000885. doi:10.1136/rmdopen-2018-000885
Sjögren syndromeSjögren syndromeSjögren syndrome
based at Newcastle Hospitals NHS Foundation Trust and Newcastle University. SB
was part funded by the Birmingham Biomedical Research Centre. KH was in receipt
of Newcastle upon Tyne NHS Foundation Trust NIHR Research Capability Funding.
Competing interests SJB has provided consultancy services in the area of
Sjögren’s syndrome for the following companies: Celgene, Eli Lilly, Glenmark, GSK,
Medimmune, Novartis, Ono, Pzer, Takeda, UCB. PE has undertaken clinical trials
and provided expert advice to Pzer, MSD, Abbvie, BMS, UCB, Roche, Novartis,
Samsung, Sandoz and Lilly. WFN has undertaken clinical trials and provided
consultancy or expert advice in the area of Sjögren’s syndrome to the following
companies: GlaxoSmithKline, MedImmune, Novartis, UCB, Abbvie, Roche, Eli Lilly,
Takeda, Resolves Therapeutics, and Nascient.
Patient consent for publication Obtained.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data are available upon reasonable request.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the
use is non-commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
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