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Abstract

Background: Poor sleep health is increasingly recognized as contributing to decreased quality of life, increased morbidity/mortality and heightened pain perception. Our purpose in this study was to observe the effect on sleep parameters, specifically sleep efficiency, in rheumatoid arthritis (RA) patients treated with anti-tumor necrosis factor alpha (anti-TNF-α) therapy.

Methods: This was a prospective observational study of RA patients with hypersomnolence/poor sleep quality as defined by the Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI). Study patients underwent overnight polysomnograms and completed questionnaire instruments assessing sleep prior to starting anti-TNF-α therapy and again after being established on therapy. The questionnaire included the ESS, PSQI, the Berlin instrument for assessment of obstructive sleep apnea (OSA) risk, restless legs syndrome (RLS) diagnostic criteria, and measures of disease activity/impact.

Results: A total of 12 RA patients met inclusion criteria, of which 10 initiated anti-TNF-α therapy and underwent repeat polysomnograms and questionnaire studies approximately 2 months later. Polysomnographic criteria for OSA were met by 60% of patients. Following anti-TNF-α therapy initiation, significant improvements were observed by polysomnography (PSG) for sleep efficiency, increasing from 73.9% (SD 13.5) to 85.4% (SD 9.6) (p = 0.031), and ‘awakening after sleep onset’ time, decreasing from 84.1 minutes (SD 43.2) to 50.7 minutes (SD 36.5) (p = 0.048). Questionnaire instrument improvements were apparent in pain, fatigue, modified Health Assessment Questionnaire (mHAQ), and Rheumatoid Arthritis Disease Activity Index (RADAI) scores.

Conclusions: Improved sleep efficiency and ‘awakening after sleep onset’ time

were observed in RA patients treated with anti-TNF-α therapy.

Keywords

anti-TNF obstructive sleep apnea polysomnogram restless legs syndrome rheumatoid arthritis sleep

Introduction

Sleep abnormalities including both poor sleep quality and primary sleep disorders are common in rheumatoid arthritis (RA) patients and other inflammatory arthropathies [Taylor-Gjevre et al. 2010, 2009; Da Costa et al. 2009; Abad et al. 2008; Hirsch et al. 1994]. In the past, sleep difficulties had been in large part attributed to articular pain and stiffness. Currently, there is a greater appreciation for the contribution of other factors, including depression, psychosocial stress and extent of inflammatory disease activity [Wolfe et al. 2006; Drewes et al. 2000].

Inadequate sleep has been recognized as associated with and contributing to a poorer quality of life, an increase in morbidity/mortality and heightened pain perception [Cappuccio et al. 2010; Moldofsky, 2010; Chhangani et al. 2009]. There is a growing recognition among clinicians of the importance of sleep in global patient care. Recently, in some RA clinical trials, a sleep assessment questionnaire has been included as an outcome measure of interest [Wells et al. 2009, 2008].

The role of molecular inflammatory mediators in sleep physiology and disorders is a subject of intense study [Kapsimalis et al. 2005; Krueger et al. 2003]. Several investigators have reported on the pathogenic implications of increased tumor necrosis factor alpha (TNF-α) in obstructive sleep apnea/hypopnea syndrome [Riha et al. 2005; Ciftci et al. 2004]. A functional alteration in TNF-α has also been suggested to play a pathogenic role in the development of narcolepsy [Himmerich et al. 2006]. Disturbance of TNF-α diurnal patterns have been observed in sleep deprivation and chronic insomnia in humans [Irwin et al. 2006; Vgontzas et al. 2002].

Anti-TNF-α strategies are increasingly employed in the treatment of RA. Although Wolfe and colleagues reported no differences in sleep questionnaire instrument scores based on treatment with anti-TNF therapy in a large cohort of RA patients [Wolfe et al. 2006]; there has been little published data on objective overnight sleep study or polysomnographic (PSG) parameters in RA patients treated with anti-TNF-α therapy [Zamarron et al. 2004], and no published data in patients treated longer than a first infusion. In this study, we examine PSG parameters and particularly sleep efficiency as a marker of general sleep health, in RA patients before and a time interval following initiation of anti-TNF-α therapy. This primary outcome measure of sleep efficiency was selected as a reflection of general sleep health not specific to a particular sleep disorder.

Patients and methods

RA patients attending a single site university-based rheumatology practice who were about to initiate anti-TNF-α therapy for their RA were screened for potential participation. Inclusion criteria included: a rheumatologist’s diagnosis of RA, a therapeutic plan a priori for initiation of an anti-TNF-α agent and either (a) an abnormal Pittsburgh Sleep Quality Index (PSQI) score (6 or greater) [Buysse et al. 1989] and/or (b) an abnormal Epworth Sleepiness Scale (ESS) score (greater than 10, Johns, 1991). Exclusion criteria included age less than 18 years or pregnancy within the last 6 months.

Those who met the criteria were invited to participate in this study. Study participation involved both completing a questionnaire and undergoing an overnight PSG study at two time points; prior to initiation of anti-TNF-α therapy and a second PSG study and questionnaire 1–3 months after initiation of anti-TNF-α therapy. Patients were screened for participation eligibility during their clinic visits. With consideration of sleep laboratory research scheduling logistics, patient inclusion was based on the screening questionnaire and repeat assessment of ESS and PSQI scores were obtained on the nights of the PSG studies. The values obtained in temporal proximity to the PSG study are those included in the results.

Informed consent was obtained from each participating patient. This study was in compliance with the Declaration of Helsinki, approval from the institutional medical ethics board was obtained.

The questionnaire instrument included 10 cm visual analogue scales (VASs) for pain (0–10 from ‘no pain’ to ‘pain as bad as it could be’ as anchors), fatigue (0–10 from ‘fatigue is no problem’ to ‘fatigue is a major problem’ as anchors), and global function (0–10 from ‘very poorly’ to ‘as well as I could hope for’ as anchors), the modified Health Assessment Questionnaire (mHAQ) [Pincus et al. 1983] with a score range of 8–32 increasing with greater impairment, the Rheumatoid Arthritis Disease Activity Index (RADAI) [Fransen et al. 2000], higher scores reflect greater disease activity, the Short Form 36-item Health Survey (SF-36) Quality of Life measures (Physical Component Summary [PCS] and Mental Component Summary [MCS] [Ware et al. 1998] with lower numbers associated with poorer scores), Centers for Epidemiologic Studies-Depression (CES-D) [Radloff, 1977] score which rises with depressive symptoms, a validated two-item psychosocial stress measure [Littman et al. 2006] (scores range from 0 to 12 increasing with increased stress), the ESS [Johns, 1991], scores range from 0 to 24, greater than 10 is considered consistent with hypersomnolence, the PSQI [Buysse et al. 1989], scores range from 0 to 21, a score greater than 5 is considered indicative of poor sleep quality, the Berlin assessment for risk of obstructive sleep apnea (OSA) [Netzer et al. 1999], scored to arrive at either high-risk or low-risk status, and the 2003 International Restless Legs Syndrome Study Group (IRLSSG) diagnostic criteria [Allen et al. 2003] (scored dichotomously as meeting or not meeting criteria). Information on patient age, gender, marital status, body mass index (BMI, in kg/m²), and medications were recorded.

The SF Health Outcomes^TM Scoring Software was employed to determine the quality of life scores.

The subjects were studied overnight in the sleep lab using the standard 15 channel PSG (Sandman Elite version 8.0 sleep diagnostic software, Ottawa, Canada). Established protocols were used for all PSG studies [Iber et al. 2007]. This included electroencephalogram (EEG, three channel), electro-oculogram (two channels), electromyogram (chin and leg), electrocardiogram, heart rate, snoring, thermistor airflow, nasal pressure airflow, oxygen saturation, chest wall motion, and abdomen motion. The PSG interpretation was performed and verified by a single blinded reviewer.

Specific data collected from the PSG study for each patient included the following: sleep efficiency (amount of time asleep/total time in bed, normal is 85–90%) [Hirshkowitz, 2004], apnea/hypopnea index (AHI; the number of apneas and hypopneas per hour of sleep, normal is less than 5) [American Academy of Sleep Medicine Task Force, 1999], respiratory disturbance index (RDI; the number of respiratory effort related arousals plus AHI per hour of sleep, normal is less than 10) [Peker et al. 1999], periodic limb movements (PLMs; normal is less than 5) [Montplaisir et al. 2000], arousals, total and per hour (an arousal is an abrupt change from sleep to wakefulness or from a deeper stage of nonrapid eye movement [NREM] sleep to a lighter stage as evident on EEG) [Iber et al. 2007], sleep latency (duration of time it took to achieve sleep, normal is about 10 minutes) [Carskadon et al. 2000], number of sleep stage transitions per hour, wake after sleep onset (WASO; the amount of time awake between sleep onset and final awakening) time in minutes, percentage of time in each of stage one, stage two, slow wave and rapid eye movement (REM) stages of sleep.

Statistical analysis

Data entry and analysis utilized SPSS v. 14.0 software. Descriptive statistics were used to summarize data. Means with standard deviations were calculated for continuous data and frequencies were calculated for categorical data. Comparison at the two different study time points within individual patients was performed with paired t-tests (two-tailed) and nonparametric testing with Wilcoxon signed ranks test. Pearson’s correlation coefficients were used to evaluate relationships between change in disease activity measure scores and PSG measures.

The primary outcome measure for this study was percentage sleep efficiency. Employing previously published standard deviation data [Drewes et al. 2000], an alpha of 0.05 and a beta of 0.20 (power of 80%) we required an estimated minimum of 10 patients per group to detect a minimum difference of 10% in sleep efficiency [Campbell et al. 1995].

Results

Of 21 consecutive RA patients about to start anti-TNF-α therapy and meeting inclusion criteria, 12 patients agreed to participate. No patients had been previously assessed by a sleep physician or diagnosed with a sleep disorder. The mean age of participants was 57.6 years (SD 10.5), 4 (33.3%) patients were male, 11 were married or cohabitating. The mean BMI was 30.9 kg/m² (SD 6.2). The BMI was slightly higher in patients categorized as high risk by Berlin questionnaire scoring (32.19 kg/m², SD 7.13), compared with those categorized as low risk (28.11 kg/m², SD 4.12, p = 0.39).

Ten of the twelve patients in the study group went on to initiate anti-TNF-α therapy. Two patients elected not to start anti-TNF-α therapy and withdrew from further participation in the study. Data from these two patients was not included in the comparison analysis. The mean time period between studies was 3.3 months (SD 1.89; range 1–7 months). The mean time interval between initiation of anti-TNF-α therapy and the second PSG study and questionnaire application was 1.8 months (SD 1.00; range 1–4 months). The anti-TNF-α therapy initiated was etanercept in 9 of 10 cases and adalimumab in 1 case. Questionnaire scores before and following anti-TNF-α therapy initiation are compared in Table 1. Comparison of PSG parameters between pretreatment and posttreatment initiation studies are outlined in Table 2. No significant correlations were identified between change in RADAI scores and change in sleep efficiency, AHI, respiratory disturbance index, sleep latency, wake after sleep time, or percentage of REM sleep.

Table 1.

Comparison of variables before and following anti-TNF therapy initiation.

Variable	Pre-anti-TNF-α (n = 10; mean ± SD)	Post-anti-TNF-α (n = 10; mean ± SD)	p-value*	95% CI**
Pain	6.3 (±1.9)	4.4 (±2.1)	0.018 (0.011)	0.427, 3.451
Fatigue	7.6 (±2.5)	5.8 (±2.1)	0.001 (0.005)	0.953, 2.709
Global function	6.2 (±2.4)	6.7 (±1.9)	0.640 (0.721)	−2.443, 1.583
mHAQ	15.3 (±3.1)	13.0 (±2.7)	0.034 (0.046)	0.220, 4.380
RADAI	5.9 (±1.7)	4.5 (±1.3)	0.048 (0.041)	0.0140, 2.646
Stress	6.4 (±2.3)	6.1 (±1.9)	0.343 (0.317)	−0.379, 0.979
CES-D	16.3 (±8.8)	15.9 (±9.3)	0.689 (0.682)	−1.791, 2.59
SF-36 PCS	30.77 (±8.13)	31.94 (±7.40)	0.392 (0.575)	−4.133, 1.781
SR-36 MCS	49.97 (±9.51)	47.65 (±12.20)	0.328 (0.575)	−2.760, 7.400
ESS	12.5 (±5.4)	11.2 (±3.8)	0.321 (0.305)	−1.502, 4.102
PSQI	10.4 (±3.4)	10.8 (±3.9)	0.462 (0.431)	−1.578, 0.778
% high risk for OSA per Berlin	70	70	1.000	0, 40.2
% meeting criteria for RLS	30	90	0.014	NA

p-value from Wilcoxon signed ranks test in brackets.

95% confidence intervals (CIs) of difference of mean between pre-anti-TNF and post-anti-TNF measures.

BMI, body mass index; mHAQ, modified Health Assessment Questionnaire; RADAI, Rheumatoid Arthritis Disease Activity Index; SF-36, Short Form 36-item Health Survey; PCS, Physical Component Summary; MCS, Mental Component Summary; CES-D, Center for Epidemiologic Studies-Depression; ESS, Epworth Sleepiness Score; PSQI, Pittsburgh Sleep Quality Index; OSA, obstructive sleep apnea; RLS, restless legs syndrome; NA, not applicable as width of CI extends beyond 0 and 100.

Table 2.

PSG comparisons before and following anti-TNF therapy initiation.

PSG characteristic	Pre-anti-TNF-α group (n = 10; mean ± SD)	Post-anti-TNF-α (n = 10; mean ± SD)	p-value*	95% CI**
AHI	13.0 (±13.6)	14.6 (±20.7)	0.647 (0.484)	−9.248, 6.048
RDI	16.4 (±17.7)	18.3 (±23.7)	0.611 (0.646)	−10.223, 6.363
PLM	18.3 (±24.8)	20.2 (±22.9)	0.502 (0.514)	−8.002, 4.222
Arousing PLM	4.4 (±9.5)	6.8 (±13.2)	0.135 (0.214)	−5.609, 0.889
% Sleep efficiency	73.9 (±13.5)	85.4 (±9.6)	0.031 (0.047)	−21.816, −1.324
Arousals total	132.2 (±103.8)	187.9 (±185.8)	0.207 (0.285)	−148.469, 37.069
Arousals/hour	26.2 (±24.3)	32.0 (±35.2)	0.341 (0.386)	−18.831, 7.251
Sleep latency (min)	34.6 (±44.2)	12.7 (±9.5)	0.179 (0.241)	−12.086, 55.766
Sleep stage transitions/hour	26.51 (±11.64)	23.51 (±10.01)	0.389 (0.575)	−4.498, 10.488
WASO (min)	84.10 (±43.24)	50.69 (±36.48)	0.048 (0.059)	0.295, 66.525
% stage one	21.22 (±14.08)	16.56 (±6.74)	0.230 (0.386)	−3.535, 12.855
% stage two	47.17 (±7.61)	50.71 (±4.47)	0.185 (0.241)	−9.116, 2.036
% slow wave	18.03 (±11.10)	16.33 (±7.42)	0.414 (0.445)	−2.787, 6.187
% REM	13.57 (8.37)	16.36 (3.06)	0.360 (0.333)	−9.333, 3.753

p-value from Wilcoxon signed ranks test in brackets.

95% confidence intervals (CI) of difference of mean between pre-anti-TNF RA group and post-anti-TNF RA group.

PSG, polysomnographic; SD, standard deviation (in brackets); AHI, apnea/hypopnea index; RDI, respiratory disturbance index; PLMs, periodic limb movements; WASO, wake after sleep onset time; REM, rapid eye movement.

Medication use which may impact on sleep was reviewed for each patient. Of the 10 patients completing the study, two were receiving prednisone 5 mg/day, two patients received anti-depressants, prednisone ≤10 mg/day, and a benzodiazepine, one patient each received opioid analgesics and zopiclone, and one received opioid analgesics, antidepressants and a benzodiazepine. There were no changes in such medication use between sleep studies.

Discussion

It has been recognized that there are multiple internal and external influences which contribute to global sleep health. In this study of RA patients before and after anti-TNF-α therapy initiation we observed significant improvement in pain, fatigue, mHAQ and RADAI scores. In terms of PSG changes, sleep efficiency and WASO time both exhibited significant improvement. A trend towards improvement in sleep latency was also observed. These observations are consistent with Zamarron and colleagues’ findings with infliximab in the time immediately following the first infusion [Zamarron et al. 2004] and are supportive of a contributing role for TNF mediation in sleep in this population.

In the questionnaire component of this study, 70% of our participants were identified to be at high risk of OSA by Berlin score. On the PSG studies the mean AHI (normal < 5) was elevated at 13 with a range of 0 to 42.9. A total of 60% of participants had AHI >10 consistent with OSA. This finding is in keeping with previous reports of high prevalence of OSA risk in RA patients [Taylor-Gjevre et al. 2010; Reading et al. 2009]. No significant differences in mean AHI scores were apparent between the two study points.

Interestingly, despite improvement in subjective fatigue, and sleep efficiency by PSG, we did not observe any differences in the ESS or PSQI scores following anti-TNF treatment. These observations are similar to Wolfe and colleagues’ findings with other sleep assessment questionnaires [Wolfe et al. 2006]. It is not clear why these questionnaire scores were essentially unchanged. However, as hypersomnolence is often related to OSA, the ESS may be reflecting the lack of change in the AHI scores and the presence of sleep apnea in a majority of participants. Further, the depression scores (CES-D) were unmodified, and an elevated CES-D score has been identified as an independent predictor for elevation in PSQI scores in RA patients [Taylor-Gjevre, 2011]. The persistent abnormalities of these subjective sleep scores may reflect persistent underlying psychosocial factors contributing to the patient’s perception of their sleep.

We also noted an increase in the percentage of patients meeting restless legs syndrome (RLS) diagnostic criteria post anti-TNF-α therapy initiation. Despite a small increase in periodic limb movements (PLMs) and arousing PLMs, we suspect this finding is by chance. Upon review of individual responses, we saw that of the six patients newly meeting all four RLS criteria at the second PSG study, three of them had met some but not all of the four criteria at the time of the first study. There is little published evidence of a causal association between biologic therapy and RLS, although there has been one reported case temporally associated with the use of interferon-α [LaRochelle et al. 2004]. Further evidence would be required to support such a causal linkage.

This study is uncontrolled and confined to RA patients before and after initiation of anti-TNF-α therapy. Without similarly timed PSG studies in a normal control group to compare variation in PSG measures over time in non-RA patients, it is difficult to be confident in an observation of change. It has been recognized that immediate day-to-day variation in PSG parameters may be substantial, and the influence of the ‘first-night effect’ of sleeping in unfamiliar settings, has been suggested to play a role in such a variation. However, in this study, the separation in time between the pre- and post-initiation PSGs we felt to be sufficient to create similar conditions for both study nights. It is unlikely that improved sleep parameters on the second PSG study could be attributed to overcoming a ‘first-night effect’. In addition, general population data would suggest yearly variation in sleep efficiency in the absence of interventions to be relatively minimal [Knutson et al. 2007]. The small size of the study population also limits the detectable effect size and the capacity to employ multivariate analyses in evaluation of determinants of poor sleep.

As the anti-TNF-α therapy employed in the majority of our patients was etanercept, our findings may not be generalizable to RA patients on other forms of anti-TNF-α therapy with different mechanisms of action. Additionally, our patients had generous BMIs, high depression (CES-D) scores, and pre-existing poor sleep as measured by questionnaire instruments; characteristics which may influence comparability with other populations.

In conclusion, in this study of RA patients we observed improvement in the objective PSG parameters of sleep efficiency and awakenings following initiation of anti-TNF-α therapy. These results lend support to the hypothesis that TNF-α plays a role in mediation of sleep health in RA patients. However, given the multitude of other potential mediators both internal and external that may likewise exert effects, it is likely that TNF activity is one of many contributing to influence sleep physiology. In addition, in this study we utilized polysomnography to obtain objective measurements of sleep health in RA patients. Future and larger studies would be valuable to further elucidate both the role for specific PSG measures in RA clinical research as well as the potential impact of anti-TNF-α or other therapeutic strategies on sleep physiology and behavior.

Footnotes

Funding

This study was funded by an internal grant from the Royal University Hospital Foundation Fund.

Conflict of interest statement

The authors have declared no conflict of interest.

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Improved sleep efficiency after anti-tumor necrosis factor α therapy in rheumatoid arthritis patients

Abstract

Keywords

Introduction

Patients and methods

Statistical analysis

Results

Discussion

Footnotes

Funding

Conflict of interest statement

References