Title: Patients receiving Anti-TNF Therapies Experience Clinically Important Improvements in
Rheumatoid Arthritis (RA) Related Fatigue – Results from the British Society of Rheumatology
Biologics Register for RA.
Authors: KL Druce1; GT Jones1; GJ Macfarlane1;N Basu1
1
Epidemiology Group, The Institute of Applied Health Sciences, University of Aberdeen
Address for corresponding author and reprint requests:
Ms Katie L Druce
Epidemiology Group, The Institute of Applied Health Sciences, University of Aberdeen
1.007 Polwarth Building, Foresterhill, Aberdeen, AB25 2ZD
Email: k.druce@abdn.ac.uk
Short title: RA-Fatigue improves with Anti-TNF therapies
Keywords: Fatigue; Rheumatoid Arthritis; Anti-TNF
1
Objectives: Pro-inflammatory cytokines, such as TNFα, are important in the pathogenesis of fatigue
in conditions such as rheumatoid arthritis (RA). This study aimed to determine whether fatigue
improved in a cohort of RA patients, with clinically relevant fatigue, commencing anti-TNFα therapy
and, if so, to identify predictors of improvement.
Methods: Participants recruited to a long-term observational cohort study (the British Society for
Rheumatology Biologics Register for RA, BSRBR-RA) provided information on fatigue (SF36 Vitality).
The prevalence of high baseline fatigue (SF36 Vitality ≤12.5) was calculated and improvements,
considered as a) absolute values and b) improvement from severe to non-severe fatigue (SF36
Vitality >12.5), examined six months subsequently. A comprehensive set of putative predictors of
fatigue improvement were evaluated using multivariable logistic regression.
Results: In 6835 participants, the prevalence of high baseline fatigue was 38.8%. Of those with
severe fatigue, 70% reported clinically relevant improvements and 66% moved to the non-severe
fatigue category (i.e. “improvers”). The mean change for improvers was three times the minimum
clinically important difference for improvement (33.0 units). Independent baseline predictors of
improvement were: female sex (odds ratio 1.3, 95%CI 1.1-1.7), not being unemployed due to illhealth (1.5,1.2-1.7), low disability (1.2,1.001-1.5), seropositivity (1.2,0.98-1.4), not using steroids
(1.2,1.03-1.5), no history of hypertension (1.4,1.1-1.6) or depression (1.3,1.1-1.5) and good mental
health (SF 36 mental health >35; 1.4,1.2-1.7).
Conclusions: Fatigued RA patients reported substantial improvements in their fatigue after
commencing anti-TNFα therapy. Further, a number of clinical and psycho-social baseline factors
identified those most likely to improve, supporting future stratified approaches to RA-fatigue
management.
2
Introduction
Up to 80% of Rheumatoid Arthritis (RA) patients experience significant fatigue[1] which they
consider to be equally, if not more, important as pain [2, 3]. Indeed the symptom is recognised as a
principal determinant of poor quality of life (QOL) and work disability among patients [4-6].
Unfortunately, there are no accepted therapies for this burdensome symptom. The aetiology of RA
related fatigue likely comprises multiple processes [7]. Biologically, pro-inflammatory cytokines,
known to be pivotal to the development and progression of RA [8, 9], have also been shown to
impact upon fatigue [10-13]. TNFα is one such cytokine which has also been found to induce fatigue
in animal models [13, 14] and to occur in excess in conditions such as Chronic Fatigue Syndrome
(CFS) [14, 15].
Although anti-TNFα therapies are effective at reducing inflammation in RA, the evidence of a link
between TNFα production and fatigue suggests that these therapies could also provide some relief
for those RA patients experiencing high levels of fatigue. Whilst improvements in fatigue have been
noted in patients using traditional DMARDs, initial evidence suggests that the effect of anti-TNF
therapies on fatigue is superior [1, 6, 16-19]. The results from existing RCTs show only a modest
benefit of anti-TNF therapies on fatigue[18], however, to date, no studies which have reported antiTNFα-associated improvements in fatigue have focused on evaluating the response in the target
population; those experiencing clinically relevant symptoms. Given the multi-factorial origin of
fatigue, it is likely that the response of fatigue to treatment will vary between patients. However, to
date, no study has examined whether certain patient characteristics are able to identify those most
likely to improve. Further, most prior work pertains to randomised control trials (RCTs), rather than
observational “real-life” studies such as cohort studies.
We therefore sought to determine the improvement of fatigue in a large cohort of highly fatigued
RA participants, commencing anti-TNFα therapy for their active disease. Furthermore, we aimed to
determine predictors of fatigue improvement among these participants.
3
Patients and Methods
Data from 13122 participants, recruited between October 2000 and November 2008, were obtained
from the British Society for Rheumatology Biologics Register for RA (BSRBR-RA). Data collection
methods have been described elsewhere [20], but briefly it is a large prospective register of RA
(Physician diagnosed or ACR criteria [21]) subjects commencing anti-TNF therapy with Infliximab,
Etanercept or Adalimumab, from across the UK. Data collection remains ongoing for a number of
biological drugs. A comprehensive set of demographic, clinical and psycho-social variables were
measured at commencement of therapy and every six months subsequently. Ethical approval was
granted to the BSRBR-RA by the North West Multi-centre Research Ethics Committee (MREC
00/8/53).
Data captured comprised demographic, psycho-social and clinical variables. Using a self-report
questionnaire, age, sex, ethnicity, and work status were captured at baseline. At baseline and six
months, participants also provided information on:
Short form 36 (SF 36) Vitality – Fatigue was measured using the Short form 36 (SF 36) Vitality scale,
which has been validated for use in RA populations [22, 23]. In line with recommended strategies
[24, 25], missing SF 36 Vitality items were replaced with the mean of completed domain items,
provided 50% of items were reported. In this analysis, 358 participants required item replacement
for baseline or follow up fatigue (5.2%). Full domain scoring was also completed as per the
recommendations. Scores were transformed into a 0-100 scale, where a higher score indicates a
better health state. Change in fatigue was calculated between baseline and follow up. Suggested
minimum clinically important differences (MCID) in SF 36 domains range from 5 to 10 units [26, 27],
for this study a conservative MCID of change ≥10 fatigue units was adopted.
As no published definitions of severe fatigue were available, severe fatigue was defined as ≤5th
percentile from a general population sample. The general population data was obtained from 1604
4
participants randomly selected from health board records, as part of a large scale general population
postal survey conducted in the Grampian Region of Scotland[28]. The sample, which were age and
sex matched to those in the current study, reported data on a variety of health measures, including
the SF 36 Vitality 0-100 scale. Severe fatigue was consequently defined as a score of less than or
equal to 12.5 units on the SF 36 Vitality 0-100 scale.
SF 36 – The SF 36 provided additional health status information pertaining to the pain, role physical,
role emotional, physical functioning, social functioning, general health and mental health
experienced over a recall period of one month. Missing SF 36 items imputation, scoring and 0-100
scale transformation, were conducted as for the vitality scale [24, 25]. Studies show the SF 36 has
good internal consistency, construct validity and sensitivity in RA populations [22, 23]. For this study
the pain, role physical, role emotional and mental health domains were used.
High SF 36 scores were defined using the same general population study from which the severe
fatigue value was derived. Definitions of high SF 36 domains, included in the logistic regression
analysis, were therefore scores ≤ 5th percentile of the general population data; high pain ≤22, high
role physical ≤25, high role emotional ≤33.3 and high mental health ≤35.
Health Assessment Questionnaire Disability Index (HAQ-DI) - This measure of disability encompasses
eight categories of functioning – dressing, rising, eating, walking, hygiene, grip, reach and usual
activities. Participants rated their ability to complete given tasks and scores are calculated on a scale
of 0 (no disability) to 3 (completely disabled). The HAQ was developed and validated with RA
patients [29, 30].
All other variables were reported by clinicians using a questionnaire which comprised:
5
Clinical status - data was obtained on the length of disease duration, disease activity score (DAS28;
baseline and follow up), the presence of erosions, history of early morning stiffness lasting longer
than one hour, rheumatoid factor status, and body mass index (BMI).
Medication – In addition to the type of anti-TNFα therapy, information on the current use of disease
modifying anti-rheumatic drugs (DMARDs), steroids, non-steroidal anti-inflammatory drugs
(NSAIDs), beta-blockers, thyroxine, antidepressants and analgesics upon entry to the register was
obtained.
Co-morbidities – clinician reported histories of hypertension, stroke, chronic obstructive pulmonary
disorder (COPD), depression and renal disease were also gathered.
For this study, participants were eligible if they were commencing anti-TNFα for the first time and,
after following mean-value imputation methods for missing data, had measures of fatigue, SF 36
vitality, at baseline and at six month follow up.
Comparison cohort
The BSRBR-RA also provided data for 3774 participants recruited to a DMARD comparison cohort.
Subjects with confirmed RA (Physician diagnosed or ACR criteria [21]) who were not commencing
anti-TNFα therapy were recruited and followed the same protocol as the anti-TNF cohort. To be
eligible for this analysis the DMARD comparison cohort, participants were required to have
measures of fatigue (SF 36 vitality) at baseline and at six month follow up. The data of those DMARD
participants with high baseline fatigue was compared against changes in fatigue observed in the
anti-TNF cohort. The available variables for the DMARD cohort mirrored those above, however for
6
the purposes of comparison it was age, sex, DAS28, disease duration, HAQ and baseline and followup fatigue which were of interest for this analysis.
Analysis
The prevalence of severe fatigue was examined and among only those with severe fatigue (SF 36
Vitality 0-100 ≤12.5), changes in fatigue at 6 months were examined. Changes were considered in
two ways a) as absolute values of change and b) as improvement from severe fatigue status to nonsevere fatigue status (SF 36 Vitality 0-100 >12.5) and in two groups a) all those with severe fatigue
and b) those who improved to a non-severe fatigue status at 6 months.
The prevalence of severe fatigue was then examined in the DMARD comparison group. Among
those with severe fatigue, the changes in fatigue reported between baseline and six months were
determined as an absolute value of change and presented as a percentage of the changes reported
by anti-TNF commencers.
All baseline variables were offered to univariate logistic regressions. Given the large number of
putative associations, a relaxed p-value - p≤0.2 – was selected to determine variables which may be
associated with improvement in fatigue after adjustment by other predictors and therefore be
eligible to be offered to a forward stepwise logistic regression model. Variables returned as
significant univariate predictors were re-coded to ensure all variables were associated with an
increased likelihood of fatigue improvement. Variables were eligible for inclusion in the stepwise
multivariable model p≤0.1 and were excluded at p>0.15 The model was evaluated by allocating
points to each participant based upon the number of positively associated variables present (1 point
per variable). Participants were then categorised according to total number of variables present and
proportion of participants improved and mean reductions in fatigue reported for each category.
7
The SF 36 functioning scales were excluded from analysis due to perceived overlap with HAQ-DI and
the SF 36 general health domain was excluded because it was felt to be too broad a construct. All
analysis was conducted using Stata 12.1 (College Station, Tx).
RESULTS
6835 participants were eligible for the study, baseline characteristics are shown in Table 1. In brief,
participants tended to be white females, with an average age of 57 and disease duration of just over
13 years. As expected, DAS and disability were high (mean 6.57 (S.D 0.98) and 2.03 (0.56),
respectively). Finally, overall health state was poor, evidenced by low scores on SF 36 domains,
including pain (21.1 (15.8), fatigue (24.8 (19.7) and general health (30.6 (18.4)).
TABLE 1 to be inserted here
Severe fatigue was reported by 38.8% of the sample, and, as severe fatigue was defined using the
5th percentile of general population data, it was therefore almost 8 times more commonly reported
than in the general population study.
Of those with severe baseline fatigue (characteristics shown in Table 1), approximately 70% reported
a clinically relevant improvement of at least 10 units, with the average change in fatigue reported as
an improvement of 22.5 units (SD 20.5). Furthermore, 66.5% of those with severe fatigue improved
to a non-severe fatigue state at follow up. The mean change in fatigue reported by improvers was
33.0 (SD 16.8); three times greater than the MCID for improvement.
Comparison cohort
Of the participants recruited to the DMARD cohort, 2375 were eligible for the comparison group.
Participants were slightly older than anti-TNF commencers (mean 60.7, SD 11.8), with a shorter
disease duration (9.9, 10.5) lower disease activity (5.02, 1.30) and less disability (1.45, 0.76). They
reported less fatigue, on average (36.9, 20.3) and a smaller proportion reported severe fatigue at
baseline (16.9%, n=403). For DMARD participants with severebaseline fatigue, the average
8
improvement in fatigue between baseline and the six-month follow up was 13.3 units; reflecting
59% of the improvement reported by anti-TNF participants with severe fatigue, and 40% of the
improvement reported by anti-TNF ‘Improvers’. As a result and 60% of the improvement reported by
anti-TNF ‘Improvers’ was above the value likely accounted for by regression to the mean.
Predicting those who will improve in fatigue after commencing anti-TNFα therapies
Variables offered to the stepwise logistic regression, after using univariate analysis to determine
individual significance (i.e. those associated at p≤0.2) , were mental health, role emotional, role
physical, HAQ, sex, BMI, work status, histories of depression, hypertension, COPD and renal disease,
the use of DMARDs, steroids, anti-depressants and beta blockers and Rheumatoid Factor status
(RF+). No difference in improvement was observed between the use of Infliximab (INF),
Adalimumab (ADA), or Etanercept (ETA) (INF vs ADA p=0.37; INF vs ETA p=0.41; ADA vs ETA p=0.92;
all univariate analysis shown in Supplementary Table 1.).
Eight variables were identified as independent predictors of fatigue improvement; good mental
health (SF 36 Mental health>25), no history of hypertension, or of depression, no use of steroids, a
work status other than “not working due to illness or disability”, low disability (HAQ <2), female sex
and being RF+ve (Table 2).
TABLE 2 to be inserted here
For the purposes of model evaluation, participants were grouped in scores of 0-2, 3-5, or 6-8
positively associated variables present (e.g. no history of depression and female sex). Improvement
was high in all groups; however participants who were positive for ≥6 variables reported the largest
absolute improvement in fatigue. Furthermore, 75% of those positive for ≥6 variables, reported
improvement in fatigue (Figure 2).
Image 1.tif to be inserted here
Figure 1 – Proportion of participants who improve and mean improvement in fatigue for those with
0-2, 3-5 and 6-8 variables present.
9
DISCUSSION
Severe fatigue was reported 8 times more commonly in RA patients initiating anti-TNF therapy
population than within the general population. Yet, more than two thirds of those patients
experienced important improvements in fatigue, which, on average, were in excess of 3 times the
MCID following therapy. In addition, a number of independent baseline predictors of improvement
were identified, including a) a good disease status, as reflected by a low HAQ, non-use of steroids
and avoidance of unemployment due to ill health b) absence of past depression and good current
mental health and c) female sex.
A number of potential limitations should be considered when interpreting these results. First, the
use of cohort data is often associated with concerns of bias of the sample following loss to follow up;
however, no clinically meaningful differences for core variables such as age, sex, fatigue and disease
activity (data not shown) were observed between those who were included and those lost to follow
up.
Second, the use of SF 36 Vitality is associated with debate as to whether fatigue and vitality sit on a
continuum [31] and it is noted that this measure cannot account for the impact of fatigue on daily
life. However, the scale, validated in RA populations, provides a global fatigue score, for which good
internal consistency, sensitivity and construct validity have been found [22, 31]. Although availability
and inclusion of an alternative measure of fatigue would have validated these results, we provide
data using a scale which, crucially, is sensitive to change following biologic agents [22] and which,
given its widespread use, allows our results to comparable to other disease groups and also to the
general population.
In addition, to be eligible to commence biologics and to enroll in the BSRBR-RA Biologics cohort,our
sample had a specific disease profile of high disease activity and had RA refractory to traditional
DMARD treatments. As a result, the study is not generalizable to the entire RA population, yet, the
lack of association between fatigue improvement and baseline measures of disease status suggests
10
that the identified predictors may potentially be applicable to patients with less active disease and
severe fatigue.
Our selection of participants with the highest level of baseline fatigue for analysis raises concerns
that improvements in fatigue may be influenced by regression to the mean (RTM) [32, 33], if the
group selected for analysis are outliers (i.e. they report abnormal fatigue levels). In spite of those
with severe fatigue not being outliers ( 38.8% of the sample) we examined the potential impact of
RTM using the BSRBR-RA’s DMARD comparison cohort. As we observed that the improvements in
fatigue reported by anti-TNF Improvers were approximately 2.5 times greater than the natural
variation reported by the DMARD cohort, it was concluded that at worst only part of the substantial
improvements reflects RTM.
Furthermore, given that 80% of improvers were found to report non-severe fatigue after one year
(data not shown), swe conclude the improvements to be long-term, rather than short lived placebo
effect from commencing a new therapy. We propose that it would be of interest for future
longitudinal research to provide mechanistic insight as to how fatigue develops and changes with
reference to other disease status measures. Such insight would develop understanding of the
symptom and would potentially inform treatment options.
Within this study, we determined predictors of those who will most likely improve to a non-severe
fatigue status at six months, however, a high proportion of participants with 0-2 variables present
improved in fatigue. This highlights the multidimensional nature of fatigue and, in spite of
consideration of a broad range of relevant co-variates, the probable existence of residual
confounding, which arises from a number of variables known to be associated with fatigue, the (e.g.
sleep problems and cognitive influences [34]), were not captured by the original study and therefore
not available for this analysis.
That said, a number of previously identified associations were highlighted in this analysis, including
an association between fatigue and hypertension, likely the result of treatment by beta11
blockers[35], and being RF+ve. Within this study, classification of RA was often defined according to
the physician diagnosis alone and so although previous studies have associated seronegativity with
more severe fatigue [36, 37], we propose that the inclusion of seropositivity in the model may serve
to identify those who truly have RA, rather than those who may have been misclassified.
The importance of both history of depression and current mental health has been discussed
previously [2, 13, 38]and the suggestion that a “residual scar” is left after a depressive episode,
leaving patients vulnerable to further depression and disruptions of symptoms, is supported by our
model[2, 38]. Similar theoretical frameworks may explain why the ability to maintain work and low
levels of disability would be important predictors of fatigue improvements [39, 40].
In contrast to our results, it has been suggested that males experience more clinically significant
effects from anti-TNF due to a lack of androgens in RA [41, 42]. However, previous literature, is
conflicting and, as in this study, ( data not shown), has demonstrated that females consistently
report more fatigue than their male counterparts [43-46] and as such greater improvement may
simply be due to females having a greater capacity for change.
Notably, in spite of a wealth of literature discussing the prominence of the pain-fatigue relationship
[1, 3-5, 44, 46, 47], pain was not determined to be an individually significant predictor of fatigue
improvement. However, recent literature has found that the pain-fatigue relationship is
synchronous, or transitory [47, 48] and therefore baseline pain may have little influence on fatigue
in longitudinal analysis such as ours. Although the results from existing studies are equivocal [36, 49,
50], anecdotally both patients and clinicians may consider disease activity to determine fatigue
status [46, 51]. Given the lack of association between fatigue and measures of disease activity within
this study, we believethe importance of measuring fatigue in RA patients as an independent
12
outcome is emphasised. It is proposed that a future trial specifically assessing the impact of
anti-TNF therapy, regardless of disease activity, on fatigue would be informative.
Furthermore, as fatigue has yet to be considered as a primary end point, previous trials have not
focussed upon the most clinically relevant sub-population of RA patients. In consequence they have
tended not to recruit those most in need of fatigue treatment (evidenced by our participants’ lower
SF36 vitality, than participants in other published studies[1, 6, 16-19]) and have demonstrated a
more modest response of fatigue to anti-TNFα therapy [18], than our study. However, since the
mechanisms underlying fatigue are likely to be multi-factorial and vary between patients, such
therapy will not be appropriate for all and so the predictors identified in this study may inform strict
selection criteria for such a study.
Conclusions
Our study examined the impact of anti-TNF therapy on levels of RA related fatigue in those
presenting with clinically relevant fatigue and is the first to identify predictors of response. We have
demonstrated clinically important improvements in fatigue are common, in those with greatest
need, after commencing anti-TNFα therapy. Because of the multi-factorial origins of fatigue,
immunomodulation therapy is not suitable for all; we therefore proceeded to identify baseline
predictors of improvement. We believe predictors identified may inform stratified approaches to RA
related fatigue management by differentiating those who may benefit from immunomodulatory
approaches from others who may also require alternative interventions.
13
Key messages
Rheumatoid Arthritis patients commencing anti-TNF therapy commonly report substantial
improvements in fatigue after six months.
Patients most likely to report improvement in fatigue can be identified by baseline characteristics.
Improvement in fatigue does not seem to be related to baseline disease activity.
Acknowledgements: The authors would like to thank the University of Manchester for access to the
BSRBR-RA data and the BSR staff and Steering Committee for assistance with manuscript
preparation.
Conflicts of interest:
KD, GJT, GJM - no conflicts of interest to declare.
NB - research support and/or advisory: Pfizer, MSD, GSK
Funding statement:
This work was supported by The Institute of Applied Health Sciences, who fund KDs PhD.
The British Society for Rheumatology Biologics Register for RA (BSRBR-RA) is supported by a research
grant from the British Society for Rheumatology to the University of Manchester, which is indirectly
funded by Schering Plough, Wyeth Laboratories, Abbott Laboratories and Amgen.
14
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21
Table 1 – Baseline characteristics for participants
All eligible
Participants with
participants
severe fatigue
(n=6835)
(n=2652)
Age (years) - mean (SD)
57 (11.7)
56 (11.4)
% female (n)
76.5 (5228)
79.6 (2111)
% white (n)
96.7 (6395)
97.3 (2492)
% Not working due to illness and
30.5 (2020)
36.4 (935)
% Rheumatoid factor positive (n)
64.1 (4381)
63.02 (1670)
% Erosions (n)
62.9 (4290)
61.91 (1640)
Disease duration (years) - mean (SD)
13.4 (9.8)
13.8 (10.0)
Disease Activity (DAS 28) - mean (SD)
6.57 (0.98)
6.79 (0.93)
Fatigue (SF36) – mean (SD)
24.8 (19.7)
5.8 (5.3)
Pain (SF 36) - mean (SD)
21.1 (15.8)
14.0 (12.9)
Role physical (SF 36) - mean (SD)
19.0 (22.5)
11.2 (15.4)
Role emotional (SF 36) - mean (SD)
44.1 (36.2)
33.2 (33.8)
Physical functioning (SF 36) - mean
19.8 (20.0)
13.1 (16.1)
Social functioning (SF 36) - mean (SD)
36.3 (24.8)
23.1 (19.9)
General health (SF 36) - mean (SD)
30.6 (18.4)
23.1 (15.9)
Mental health (SF 36) - mean (SD)
55.1 (20.2)
45.1 (19.8)
Disability (HAQ) - mean (SD)
2.03 (0.56)
2.23 (0.47)
disability (n)
(SD)
22
Table 2 – Odds ratio for variables significantly associated with improvement in fatigue
in multivariable analysis (n=2414).
Variable
Mental health (SF 36)
Disability (HAQ)
Sex
Work status
Seropositivity
Steroid use
History of hypertension
History of depression
Odds-ratio (95% CI)
Poor (≤ 35)
1.0
Good (>35)
1.4 (1.2-1.7)
High (≥2)
1.0
Low (<2)
1.2 (1.001-1.5)
Male
1.0
Female
1.3 (1.009-1.7)
Not working due to illness or disability
1.0
All other work status
1.5 (1.2-1.7)
Rheumatoid factor negative
1.0
Rheumatoid factor positive
1.2 (0.98-1.4)
Using steroids
1.0
Not using steroids
1.2 (1.03-1.5)
Yes
1.0
No
1.4 (1.1-1.6)
Yes
1.0
No
1.3 (1.1-1.5)
Figure legend
Figure 1 – Proportion of participants who improve and mean improvement in fatigue for those with
0-2, 3-5 and 6-8 variables present.
23