Sleep Breath https://doi.org/10.1007/s11325-017-1592-4

NEUROLOGY • ORIGINAL ARTICLE

Restless legs syndrome and sleep quality among adult sickle cell disease patients Siraj Omar Wali 1 & Ibrahim AlQassas 2 & Roah Merdad 3 & Rajaa Alsaggaf 1 & Fatin Al-sayes 4

Received: 25 January 2017 / Revised: 16 October 2017 / Accepted: 8 November 2017 # Springer International Publishing AG, part of Springer Nature 2017

Abstract Objective The purpose of this study is to determine and compare the prevalence of restless legs syndrome (RLS) between adult patients with sickle cell disease (SCD) and non-SCD anemia. Methods This cross-sectional study was conducted from December 2013 to July 2014. Patients with SCD and nonSCD anemia were recruited from a hematology clinic at a large university hospital. Patients with secondary RLS were excluded. Data were collected on demographic features, clinical evaluations, laboratory tests, sleep quality using the Pittsburgh Sleep Quality Index, RLS symptoms using the International Restless Legs Syndrome Study Group Criteria, severity of RLS using the International Restless Leg Syndrome Rating Scale, and daytime sleepiness using the Epworth Sleepiness Scale. Results The study sample consisted of 44 patients with SCD and 45 with non-SCD anemia. The two groups were comparable in age, gender, body mass index, smoking habit, and comorbidities. Poor sleep quality was found in 63% of the SCD group compared to 53% of the non-SCD group. The prevalence of RLS among SCD group and non-SCD group was 13.6% (6/44) and 8.8% (4/45), respectively. These

* Siraj Omar Wali [email protected]

1

Sleep Medicine and Research Center, King Abdulaziz University Hospital, PO BOX 21589, Jeddah 80215, Saudi Arabia

2

International Medical Center, Jeddah, Saudi Arabia

3

Department of Family and Community Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

4

Department of Hematology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

differences, however, were not statistically significant, p > 0.05. Excessive daytime sleepiness was also similar in both groups, with the rate being 20.5 and 17.8% in the SCD and non-SCD groups, respectively. Conclusion Our study revealed that poor sleep quality and RLS were both common among adult patients with SCD; however, they did not differ significantly from patients with non-SCD anemia. Keywords Sleep . Sleep disorders . Severity . Frequency

Introduction Sickle cell disease (SCD) is a group of disorders caused by abnormal B chains in the hemoglobin of affected individuals [1]. SCD is a disease that physicians in Saudi Arabia encounter on a regular basis. It is particularly common in the eastern part of Saudi Arabia, where the rate of consanguineous marriage is high [2, 3]. Based on the Saudi Premarital Screening Program, the prevalence of SCD and SCD trait are 0.26 and 4.2%, respectively [4]. Patients with SCD often experience challenges in their overall quality of life. The degree to which they suffer from sleep problems is of importance. One important problem affecting SCD patients is pain. Because the inherited hemoglobin is less soluble, red blood cells assume a sickled shape at low oxygen tension instead of their usual biconcave disc morphology. This has two detrimental effects: first, the impaired oxygen transport leads to hypoxia, and second, the accumulation of distorted red blood cells in small capillaries leads to pain [5, 6]. Sickle cell pain manifests as an acute painful crisis, chronic pain syndrome, and neuropathic pain [6]. The pain that they experience reportedly affects their sleep. Wallen et al. evaluated 328 adult SCD patients and reported that just over 70% of them suffered from poor sleep

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quality; they also found a significant correlation between sleep disturbance and the number of acute painful events [7]. Another condition that could affect SCD patients’ sleep quality is restless legs syndrome (RLS), which affects 8.4% of the general population [8]. Restless legs syndrome is a neurological disorder in which patients experience an overwhelming temptation to move their legs at rest (mainly at night), thus impairing their quality of sleep. Although some researchers have explored the prevalence of RLS among pediatric SCD patients [9], it has not been documented for adults with SCD. Investigating the prevalence of these disorders among SCD patients may help increase the awareness of clinicians regarding the need for appropriate screening and referral to sleep medicine clinics. The aim of this study was to determine the prevalence of poor sleep quality and RLS among adult SCD patients and compare this prevalence to that found in other chronic anemia patients. The second objective was to determine whether SCD patients with RLS complain of higher excessive daytime sleepiness (EDS) and poor sleep quality compared to those without RLS. The final aim was to assess the association between potential predictors of RLS and poor sleep quality among SCD patients.

Methods Study design, participants, and ethics In this cross-sectional observational study, all consecutive stable adult patients with SCD presenting to the hematology clinics at King Abdulaziz University Hospital from December 2013 through July 2014 were invited to participate in the study. In addition, patients with other types of chronic anemia were invited as well and used as a comparison group. Trained physicians interviewed and clinically assessed each participating patient. Patients were excluded from the study if they had other forms of secondary RLS (iron deficiency anemia, renal failure, pregnancy, varicose veins, peripheral neuropathy, Parkinson disease, and multiple sclerosis), or any condition that could mimic RLS [10–12]. Accordingly, a total of 18 patients who had agreed to participate were excluded: 2 patients with chronic renal failure, 2 pregnant, and 14 patients with low serum ferritin indicating iron deficiency. After exclusions, 44 patients with sickle cell disease (SCD group) and 45 patients with chronic anemia (non-SCD group) were successfully enrolled. Patients with chronic anemia had a diagnosis of thalassemia (n = 43), hemolytic anemia (n = 1), and anemia of chronic disease (n = 1). The following data were collected: demographic features, medical history, clinical evaluation, sleep quality using the

Pittsburgh Sleep Quality Index (PSQI) [13], daytime sleepiness using the Epworth Sleepiness Scale (ESS) [14], RLS diagnosis based on the International Restless Legs Syndrome Study Group Criteria [15], and severity of RLS symptoms using the International Restless Legs Syndrome Rating Scale (IRLSRS) [16]. Additionally, anthropometric measures were obtained, and blood samples were collected. Ethical approval was obtained from the Ethics Committee at the Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

Variables Main outcomes 1. Sleep quality is assessed using the PSQI [13]. The PSQI is a reliable, valid, and standardized tool that is used to assess the quality of sleep. The tool inquiries about a patient’s sleep characteristics and behaviors over the last month. The index was scored based on the scoring scheme presented by the official PSQI developers, which is available online on their website. The PSQI global score (possible range, 0–21) is used as a continuous variable in the analysis when indicated. A higher score reflects worse sleep. Additionally, a categorical variable reflecting poor sleep quality (no = 0; yes = 1) is used in the analysis. Poor sleep quality is defined as a PSQI global score of more than 5 [13]. 2. EDS is assessed using the ESS. Epworth Sleepiness Scale, a self-administered questionnaire, was first published in 1991 and has been used to assess the level of daytime sleepiness [14]. The ESS asks people to rate, on a 4point scale (0–3), their usual chances of dozing off or falling asleep in eight different situations or activities in which most people engage as part of their daily activities, although not necessarily every day. The sum of all eight items is then calculated to give a score between 0 and 24, which is a measurement of the respondent’s average sleep propensity in those eight situations. A normal score is less than 11 [14]. 3. Restless legs syndrome is assessed using the International Restless Legs Syndrome Study Group criteria to diagnose RLS [15], and the severity of RLS is assessed using the International Restless Legs Syndrome Rating Scale (IRLS-RS) [16]. RLS diagnosis and severity are used both continuously and categorically in the analysis. The IRLSRS includes ten questions that ask respondents to indicate on a scale from 0 (none) to 4 (very severe) the severity and frequency of symptoms related to RLS. The answers are summed to generate a total score with a possible range between 0 and 40, with a higher score indicating more severe symptoms [16].

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Independent variables A variable reflecting the type of chronic anemia (SCD vs. other) is created based on information regarding the diagnosis of SCD and other chronic anemia obtained from the electronic system at KAUH during the recruitment period. Age is documented in years, BMI is calculated in kilograms per square meter, and both are used as continuous variables. Sex (male vs. female), smoking status (smoker vs. non-smoker), and history of tonsillectomy (no vs. yes) are all used as categorical variables. Serum iron (μmol/l), ferritin (ng/ml), and the level of hemoglobin (g/dl) are all used as continuous variables. Hydroxyurea, iron-chelating agents, and opioid medications are used as dichotomous variables (no = 0; yes = 1). Data analysis A descriptive overview of the characteristics of the study population and sleep outcomes is provided. The means and standard deviations for continuous variables and the numbers with column percentages for categorical variables are analyzed. Chi-square and independent sample t tests are performed, assuming equal or unequal variance, as appropriate, to compare the sleep outcomes in the SCD group and the group of participants with other types of chronic anemia. The normality and variance of all of the variables are assessed using q-q plots and Levene’s test of equality of variances. Regarding the influence of violating the normality assumption, which was present in some of our covariables, the t test is considered a robust test. Therefore, covariables with a non-normal distribution are not transformed. Chi-square tests are performed to compare those with and without RLS (among patients with SCD) who have poor sleep quality and EDS. Finally, we performed unadjusted logistic regression analysis to assess the association between a group of potential predictors, RLS, and poor sleep quality. The odds ratios and coefficients with 95% confidence intervals are presented. The statistical significance level of the analysis is set at 0.05. All statistical analyses are performed using STATA version 13 (StataCorp, College Station, TX, USA).

Results

the participants with SCD reported the use of hydroxyurea and opioids, respectively (Table 1). The use of opioids was on an as needed basis since none of the participants were in hemolytic crisis at the time of recruitment. Moreover, only two patients with RLS were using opioids, both were in the SCD group with moderate severity. Prevalence of poor sleep quality and restless legs syndrome Among the SCD group, 63% reported poor sleep quality, as measured by the PSQI; 14% had RLS, as measured by the International Restless Legs Syndrome Study Group criteria; and 21% had excessive daytime sleepiness, as measured by the ESS. The mean score of the PSQI for SCD participants was 7.75 (SD, 3.94), that of the RLS severity was 17.9 (SD, 11.9), and that of the ESS was 6.84 (SD 3.18). A comparison with the non-SCD group is presented in Table 2. The proportions of poor sleep quality, RLS, and EDS were slightly higher among the SCD group but the difference was not statistically significant. Half of patients with RLS in the SCD group had EDS, which was significantly higher than in those without RLS in the same group (14%) (p < 0.05). Furthermore, although all patients with RLS in the SCD group had poor sleep quality, they did not differ significantly from those without RLS in whom poor sleep quality was reported in 57% (p = 0.098). Among SCD patients: potential predictors of RLS and poor sleep quality In Table 3, we present the association between RLS and poor sleep quality and the following factors: age, sex, BMI, hemoglobin level, serum ferritin, serum iron, and the use of hydroxyurea, iron-chelating agents, and opioid. The result of the unadjusted logistic regression analysis shows that serum iron is significantly associated with RLS (p < 0.05). Specifically, a one-unit increase in serum iron is associated with a 10% greater chance of having RLS. In terms of poor sleep quality, age was the only significant factor (p < 0.05). A 1-year increase in age is associated with a 14% greater chance of having poor sleep quality.

Characteristics of the study population

Discussion Approximately 54% of the study sample were females, with a mean age of 27.7 years (SD, 7.3; range, 18–54) and a mean BMI of 21.3 (SD, 4.2; range, 14.4–33.9). The group of participants with SCD and the comparison group were comparable in age, sex, and BMI. The mean hemoglobin level was 8.33 g/dl. The participants from the comparison group (nonSCD group) had significantly higher levels of serum iron and ferritin. Twenty-seven percent (n = 12) and 30% (n = 13) of

This study revealed that the prevalence of RLS in SCD patients compared to the control group was 14 versus 9%. Similarly, a higher percentage of SCD patients had poor sleep quality (63%) compared to non-SCD patients (53%). Nevertheless, although the prevalence of these outcomes was higher among patients with SCD, the differences compared to the control group were not statistically significant

Sleep Breath Table 1 Characteristics of the total sample and a comparison between SCD and non-SCD groups. Continuous variables are presented as the mean and standard deviation values, and categorical variables are presented as frequency and column percentage (N = 89)

Variables

Total sample

SCD group

Non-SCD group

p*

N = 89

N = 44

N = 45

Age in years (mean ± SD) Sex (N, %)

27.7 ± 7.23

28.7 ± 6.9

26.8 ± 7.5

0.209

Male

41 (46.1%)

21 (47.7%)

20 (44.4%)

0.756

Female BMI in kg/m2 (mean ± SD) Neck circumference in inches (mean ± SD) Hemoglobin in g/dl (mean ± SD) Ferritin in ng/ml (mean ± SD)

48 (53.9%) 21.3 ± 4.2

23 (52.3%) 21.8 ± 4.6

25 (55.6%) 20.8 ± 3.7

0.273

12.3 ± 1.1

12.6 ± 1.2

11.9 ± 0.95

0.002

8.33 ± 1.3 2653 ± 2267

8.58 ± 1.2 1387 ± 1603

8.09 ± 1.4 3775 ± 2187

0.080 < 0.001

Iron in μmol/l (mean ± SD)

26.3 ± 13.3

19.8 ± 12.1

31.9 ± 11.8

< 0.001

Smoking status (N, %) Non- or ex-smoker

0.592

75 (84.3%)

38 (86.4%)

37 (82.2%)

Smoker Tonsillectomy (N, %)

14 (15.7%)

6 (13.6%)

8 (17.8%)

No Yes Use of hydroxyurea

63 (75.9%) 20 (24.1%)

24 (63.2%) 14 (36.8%)

39 (86.7%) 6 (13.3%)

0.013

No Yes Use of iron-chelating agents

17 (87.0%) 12 (13.0%)

32 (73.0%) 12 (27.0%)

45 (100%) 0 (0.0%)

< 0.001

No

52 (58.0%)

37 (84.0%)

15 (33.0%)

< 0.001

Yes Use of opioid pain medications No Yes

37 (42.0%)

7 (16.0%)

30 (67.0%)

75 (84.0%) 14 (16.0%)

31 (70.0%) 13 (30.0%)

44 (98.0%) 1 (2.0%)

< 0.001

SD standard deviation, SCD sickle cell disease, BMI body mass index *Independent sample t test and chi-square test were used to compare the two groups (SCD and other types of chronic anemia)

(p > 0.05). Our study is the first to simultaneously assess different sleep parameters in adult SCD patients and compare them with those of a comparison group (adults with other types of chronic anemia). This study also delineates the risk factors that are most likely to be predictors of RLS and of poor sleep quality among adult SCD patients. Although several studies have explored sleep disorders in children with SCD [17], there is little research in the adult age group. The prevalence of RLS among adult SCD patients was found to be 14% in our study. This rate is higher than the prevalence of RLS in the general population in Saudi Arabia (8.4%) [8]. Thus, our findings suggest that in patients with SCD, a higher clinical suspicion for RLS should be present and hence screening such patients be considered. Moreover, a study on children with SCD found that the prevalence of periodic limb movement (PLM) was 23.4%, which may indicate that children with SCD are more likely to be affected with RLS disorder than adults [9]. The potential influence of the use of opioids on RLS symptoms in our study is unknown. The use of opioids could have falsely decreased the

prevalence of RLS in patients with SCD. However, our patients were prescribed opioids only as needed, were clinically stable (not having crises at the time of interview), and, hence to our knowledge, were not using opioids. On the other hand, Wallen et al. performed a crosssectional study on 328 adult patients with SCD and concluded that 70% had poor sleep quality, as assessed using the PSQI [7]. More recently, Sharma et al. conducted a small cohort study on adults with SCD and reported a prevalence of sleep disordered breathing with symptoms of disturbed sleep or EDS among 44% of cases [18]. The findings of both studies concur with our outcomes and support the presence of a high rate of disturbed sleep in this population. Many factors have been suggested to contribute to impaired sleep among SCD patients. Seventy percent of SCD patients experience pain. Pain due to a vaso-occlusive crisis may be a major factor impairing sleep [7, 19]. We assessed other potential predictors of poor sleep quality among SCD patients, including age, sex, body mass index, and RLS. We found that age, in particular, may be an

Sleep Breath Table 2 Sleep outcomes by anemia group and prevalence OR. Continuous variables are presented as the mean with 95% CI, and categorical variables are presented as frequency and column percentage (N = 89)

SCD group

Non-SCD group

p

Prevalence OR 95% CI

N (%)

N (%)

Mean PSQI global score (mean ± SD)

7.75 ± 3.94

Poor sleep quality (PSQI > 5), N (%) Yes

7.07 ± 4.67

0.535

–

0.456

Sleep disturbance (N = 62)

20 (63.0%)

16 (53.0%)

No Restless legs syndrome

12 (37.0%)

14 (47.0%)

1.46

Mean score of IRLS-RS (N = 10) (mean ± SD) RLS (N = 88), N (%)

17.3 ± 12.6

18.7 ± 12.5

0.867

–

Yes No

6 (14.0%) 37 (86.0%)

4 (9.0%) 41 (91.0%)

0.454

1.66 (0.43–6.35)

Mean ESS score (mean ± SD) EDS (ESS ≥ 10), N (%)

6.84 ± 3.18

6.51 ± 3.37

0.642

–

Yes No

9 (21.0%) 34 (79.0%)

8 (18.0%) 37 (82.0%)

0.708

1.22 (0.42–3.53)

(0.53–4.02)

Excessive daytime sleepiness (N = 88)

The t test and chi-square test, and Fisher’s exact test are used to compare the means and proportions of the outcomes in the two groups OR odds ratio, CI confidence interval, PSQI Pittsburgh Sleep Quality Index, ESS Epworth Sleepiness Scale, EDS excessive daytime sleepiness, SCD sickle cell disease, RLS restless legs syndrome, IRLS-RS International Restless Legs Syndrome Rating Scale

important predictor of poor sleep quality (OR, 1.14 (1.01– 1.29); p = 0.034). A 1-year increase in age was found to be associated with a 14% increase in the odds of experiencing poor sleep quality. While all SCD patients with RLS experienced poor sleep quality compared to only 57% of those without RLS, this was a non-significant finding, likely due to the small size of our study. Nonetheless, SCD patients

Table 3 Unadjusted logistic regression models of RLS and poor sleep quality among SCD patients regressed on a group of sociodemographic and medical factors

with RLS reported more excessive daytime sleepiness compared to those without RLS (p = 0.037). The pathophysiology of RLS in SCD remains poorly understood. Among the predictors for RLS, serum iron seemed to be the only predictor influencing the risk of developing this nocturnal condition (OR, 1.10 (1.01–1.21); p = 0.041) keeping in mind that those with iron deficiency

Variables

Restless legs syndrome

Poor sleep quality

(N = 44)

(N = 32)

OR (95% CI)

p value

OR (95% CI)

p value

Age (years) Sex (m = 0, f = 1) BMI (m2/kg) Hemoglobin (g/dl)

1.05 (0.92–1.19) 5.27 (0.56–49.7) 1.10 (0.91–1.31) 0.47 (0.22–1.03)

0.453 0.146 0.356 0.060

1.14 (1.01–1.29) 1.71 (0.40–7.27) 1.09 (0.92–1.28) 0.56 (0.26–1.14)

0.034* 0.467 0.287 0.108

Serum ferritin (ng/ml) Serum iron (μmol/l) Use of hydroxyurea (no = 0, yes = 1) Use of iron-chelating agents (no = 0, yes = 1) Use of pain killers (no = 0, yes = 1)

1.00 (0.99–1.00) 1.10 (1.01–1.21) 3.11 (0.53–18.2) 1.03 (0.10–10.5) 1.35 (0.21–8.55)

0.338 0.041* 0.208 0.978 0.750

1.00 (0.99–1.00) 1.02 (0.96–1.10) 0.66 (0.14–3.20) 0.75 (0.13–4.13) 4.09 (0.71–23.7)

0.649 0.434 0.613 0.741 0.116

Sleep quality was assessed using Pittsburgh Sleep Quality Index (PSQI) OR odds ratio, CI confidence interval, BMI body mass index, RLS restless legs syndrome, SCD sickle cell disease *p < 0.05

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were excluded (Table 3). We found that a one-unit increase in serum iron was associated with a 10% increase in the odds of having RLS. Contrary to our findings, common scientific belief is that RLS is associated with iron deficiency. Thus, our results probably suggest a different pathophysiology for RLS than that in patients with iron deficiency. The elevated iron levels in patients with SCD and thalassemia (the major component of non-SCD group) are related to the frequent blood transfusions and hemolysis encountered (Table 1). However, this reverse relation between RLS and serum iron was also reported in two adult patients with hemochromatosis and severe RLS [20]. Magnetic resonance imaging showed that RLS symptoms might be linked to low regional brain iron rather than serum iron levels [20]. To our knowledge, brain iron stores, which may present a mechanism for RLS, have not been studied in patients with SCD. In their study, Rogers et al. found that PLMs in children were associated with low to low-normal serum iron and ferritin levels [9]. Another mechanism postulated for developing RLS in SCD patients identifies inflammation caused by vaso-occlusive episodes as the culprit. In such circumstances, iron may be released from storage sites, particularly macrophages, thus decreasing iron transport to tissues with high iron requirements, such as the brain. In short, iron regulation in SCD as a potential cause of RLS is still poorly understood and presents an exciting pathway for future research. This study has some limitations. As a cross-sectional study, we could not assess the prospective patterns of sleep disturbance among SCD patients. Although our study allows us to appreciate a likely association between SCD and sleep disorders, the findings stopped short of assessing the strength of the relationship or proving causation. Moreover, in the absence of polysomnography, sleep-disordered breathing or periodic limb movements during sleep were not evaluated. Another limitation is that pain was not formally assessed. Both polysomnography findings and objective pain assessment could contribute further to determining sleep quality in this vulnerable population. Additionally, our sample size is limited (n = 89), which means the study may have been underpowered to detect true differences. Furthermore, a sample that is derived from a single center limits generalizability. Despite its limitations, our study highlights that the burden of sleep disorders could be significant among SCD patients; therefore, appropriate screening may be considered by maintaining a thorough sleep history and analyzing sleep diaries. Improving sleep in this population is equally important as improving the patients’ quality of life to optimize health outcomes. Spreading such awareness to physicians caring for SCD patients, including hematologists and family physicians, will hasten the early detection and treatment of sleep disorders among this cohort of patients.

Acknowledgements The authors would like to thank Prof. Mohammad Qari and Prof. Gazi Damnhory, hematology consultants at King Abdulaziz University Hospital, for their great support in recruiting patients for our study. The authors would also like to appreciate the great proficiency of Mrs. Walaa Abuzahra, the research coordinator of Sleep Medicine and Research Center. Author contribution Siraj Wali: designed the study and contributed in data interpretation and in writing the manuscript Ibrahim AlQassas: contributed in data interpretation and in writing the manuscript Roah Merdad: performed the statistical analysis of the study, contributed in data interpretation and writing the manuscript Rajaa Alsaggaf: contributed in collecting and interpretation of data, and in writing the manuscript Fatin Al-sayes: designed the study and contributed in editing the whole manuscript Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. Ethical approval Ethical approval was obtained from the Ethics Committee at the Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia. Informed consent Informed consent was obtained from all individual participants included in the study.

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