pii: sp- 00100-15
http://dx.doi.org/10.5665/sleep.5328
NEUROLOGICAL DISORDERS
Lack of Association between Genetic Risk Loci for Restless Legs Syndrome and Multimorbidity András Szentkirályi, MD, PhD1 Henry Völzke, MD2,3; Wolfgang Hoffmann, MD, MPH2,4; Julianne Winkelmann, MD5,6,7,8; Klaus Berger, MD, MPH, MSc1 1 Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany; 2Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; 3German Centre for Cardiovascular Research, Partner site Greifswald, Germany; 4German Centre for Neurodegenerative Diseases (DZNE), Germany; 5Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; 6Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; 7Department of Neurology, Technische Universität München, Munich, Germany; 8Department of Neurology and Neurosciences, Stanford Center for Sleep Medicine and Sciences, Stanford University, Palo Alto, CA
Study Objectives: Multimorbidity is a risk factor for incident restless legs syndrome (RLS). In this relationship, the potential role of known genetic risk loci for RLS has not been studied. Our aim was to evaluate whether carriers of specific RLS risk alleles have higher comorbidity burden than noncarriers. Methods: The Dortmund Health Study (DHS) and the Study of Health in Pomerania (SHIP) are two independent cohort studies in Germany based on agestratified, random samples drawn from the respective population registers. DHS included 1,312 subjects and SHIP included 4,308 subjects. RLS status was assessed according to the RLS standard minimal criteria. A comorbidity index was calculated by summing the scores of the following conditions: diabetes, hypertension, myocardial infarction, obesity, stroke, cancer, renal disease, anemia, depression, thyroid disease, and migraine. Thirteen single nucleotide polymorphisms (SNP) previously associated with elevated risk of RLS were genotyped. Analyses were carried out on the pooled sample of the two studies. Results: The mean age was 50.4 ± 15.9 y, and the proportion of women was 51.4%. The mean number of comorbid conditions was 1.5 ± 1.3. In multivariable regression, the mean number of comorbidities was not significantly different between carriers of any of the RLS risk alleles and noncarriers either in the total pooled sample or in those having RLS symptoms. Conclusions: Based on these results it is unlikely that known genetic risk factors for RLS would lead to increased multimorbidity. Keywords: multimorbidity, restless legs syndrome, risk alleles Citation: Szentkirályi A, Völzke H, Hoffmann W, Winkelmann J, Berger K. Lack of association between genetic risk loci for restless legs syndrome and multimorbidity. SLEEP 2016;39(1):111–115. Significance Patients with RLS have an increased number of comorbid conditions in the general population. In order to examine whether genetic risk factors of RLS contribute to this relationship, we investigated the potential association between the presence of known genetic risk loci for RLS and higher comorbid burden in the general population. Since no significant relationship was detected, known RLS risk loci do not seem be risk factors of increased comorbidity seen in RLS patients. Further studies are required to clarify the background of the association between multimorbidity and RLS.
INTRODUCTION Many studies have reported on potential associations between restless legs syndrome (RLS) and different chronic diseases.1–20 According to our recent study, the simultaneous presence of several chronic conditions (multimorbidity) is an important risk factor for RLS.21 The etiology of the relationship between multimorbidity and RLS remains unclear. Variants in several loci encompassing the genes MEIS1, BTBD9, MAP2K5/SKOR1, PTPRD, and TOX3/noncoding ribonucleic acid have been recently identified by genome-wide association studies indicating a higher risk of RLS.22–25 We have limited knowledge about the exact function of the affected genes and their role in the development of RLS. We hypothesized that RLS risk variants may contribute to a greater comorbid burden. Therefore, we investigated whether the presence of RLS risk alleles is associated with an increased number of comorbid disease states in individuals from the general population and in those participants with RLS.
samples. The design, selection of participants, and data collection had been described previously.26,27 In brief, for DHS, the overall response at baseline was 66.9%. Assessment of RLS and co-morbidities were restricted to participants available for a face-to-face interview (n = 1,312). The final number of subjects participating in the SHIP was 4,308 (response 68.8%). All participants gave informed written consent, and the local ethics committees of the Medical Faculty at the University of Münster (for DHS) and the University Medicine of Greifswald (for SHIP) approved the study protocol.
METHODS
RLS Assessment RLS was assessed identically in both studies during face-toface interviews conducted by trained and certified interviewers with a short questionnaire that had previously been validated against physician classification 28 and already been used in prior reports.16,21,29,30 The questions followed the minimal criteria published by the International Restless Legs Syndrome Study Group.31 Participants were only classified as RLS positive if they answered all symptom questions with “Yes.”
Study Population The Dortmund Health Study (DHS) and the Study of Health in Pomerania (SHIP) are two independent population-based prospective cohort studies based on age-stratified random
Sociodemographic Data, Comorbidities, and Laboratory Measurements All data were assessed in computer-assisted face-to-face interviews (CAPI). The current medication, taken within the
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Thirteen candidate SNPs associated with elevated risk of RLS were selected on the basis of published data22–25 (Figure 1). In four cases, SNP call rate was below 92%: rs12593813 (89.5%) and rs12469063 (90.8%) in DHS and rs2300478 (84.5%) and rs4626664 (91.6%) in SHIP. The mean call rate for the other SNPs was 92.4 ± 0.17 and 94.7 ± 0.04 in DHS and SHIP, respectively. In the SHIP sample, the allele frequency of the SNP rs2300478 significantly deviated from the Hardy-Weinberg equilibrium (HWE) even after correction for multiple testing, whereas none of the other allele frequencies deviated significantly from HWE.
past seven days, was recorded and classified according to the Anatomic Therapeutic Chemical (ATC) Classification System (2006).32 The presence of diabetes, hypertension, myocardial infarction, stroke, and cancer was assessed as self-reports with specific questions asking for physician’s diagnosis of the respective condition. The phrasing of the questions and the answer categories were similar in the two studies. The classification of diabetes and hypertension was additionally based on the intake of medication. Prevalence of thyroid disease was based on medication (either thyreostatic or substitution therapy). The presence of depressive symptoms was assessed with the Center for Epidemiologic Studies–Depression (CES-D) scale33 in DHS and with the Munich-Composite International Diagnostic-Screener (CID-S)34 in SHIP. Migraine was assessed by a single question in SHIP and by a standardized questionnaire according to the International Headache Society’s new (2nd edition) classification criteria in DHS.35,36 Body weight and height were measured with shoes and heavy clothing removed. Body mass index (BMI) was calculated as mass (kg) divided by the square of height (m2). Obesity was defined as a BMI higher than 30 kg/m2. In SHIP and in a subset of DHS participants (n = 1,152) nonfasting blood samples were collected under standardized conditions for measurement of serum creatinine and hemoglobin. Renal function was estimated by glomerular filtration rate (GFR), which was calculated according to the Chronic Kidney Disease Epidemiology Collaboration formula.37 A GFR of 60 mL/min/1.73 m2 or less was considered as renal disease. Assessment of anemia was based on cutoffs for hemoglobin level (for women: 120 mg/dL; for men: 140 mg/dL) and/or medication against anemia (Anatomical Therapeutic Chemical classification “B03”). Comorbidity index as a measure of disease burden was calculated by determining the sum of the scores of the following conditions (diabetes, hypertension, myocardial infarction, obesity, stroke, cancer, renal disease, anemia, depression, thyroid disease, and migraine) following an established procedure.21
Statistical Analysis In order to increase statistical power, all analyses were performed on the pooled dataset of DHS and SHIP. To obtain normal distribution, the negative inverse of the square root of the comorbidity index was taken. Age- and sex-adjusted linear regression models were built separately for both studies to analyze the independent relationship between the number of risk alleles for each SNP and the comorbidity index. In these analyses, the presence of zero, one, or two risk alleles was entered as an independent categorical variable using zero risk alleles as the reference, whereas the comorbidity index was the dependent variable. When a recessive or dominant genetic model was used, the number of risk alleles was recoded accordingly to gain the respective dichotomized variable. The total number of risk alleles per individual was calculated by adding the number of risk alleles belonging to each of the 13 SNPs, thereby gaining a number ranging from 0 to 26. In each case two-tailed hypotheses were tested. All analyses were done with Stata 11.0 (StataCorp LP, College Station, TX, USA). Power calculations were performed for the multivariate linear regression models using the “powerreg” command. RESULTS Demographics and Basic Characteristics From a total of 5,620 available subjects, complete comorbidity data were available for 5,342 participants (DHS: n = 1,136, SHIP: n = 4,206). The mean age was 50.4 ± 15.9 y, and the proportion of women was 51.4%. The prevalence of RLS was 9.5% and the mean number of comorbid conditions was 1.5 ± 1.3. The allele frequencies for each SNP are shown in Figure 1.
Genotyping Details of genotyping and quality control have been published previously.38–40 Genomic deoxyribonucleic acid was prepared from fresh or frozen EDTA-anticoagulated blood samples. In DHS, genotyping was performed on the MassARRAY system using MALDI-TOF mass spectrometry with the iPLEX Gold chemistry (Sequenom Inc, San Diego, CA, USA), and primers were designed using MassArray AssayDesign 3.1.2.2 with iPLEX Gold default parameters. Automated genotype calling was done with SpectroTYPER 3.4 (Sequenom Inc, San Diego, CA, USA). In SHIP, 4,081 samples were successfully genotyped using the Affymetrix Genome-wide Human SNP Array 6.0 (Affymetrix, Santa Clara, CA, USA).39,40 The overall genotyping efficiency of the genomewide association was 98.6%. Genotypes were determined using the Birdseed2 clustering algorithm. Imputation of genotypes was performed with the software IMPUTE version 0.5.0 (Oxford, UK) against the HapMap II (CEU version 22, Build 36; International HapMap Consortium) reference panel using 869,224 genotyped SNPs. The total number of SNPs after imputation and quality control was 2,748,910. SLEEP, Vol. 39, No. 1, 2016
RLS Risk Alleles and Multimorbidity Figure 1 presents the mean number of comorbid conditions for each genetic risk variant for RLS in all subjects and in individuals with RLS symptoms. In multivariable linear regression models, the number of risk alleles was not associated significantly with the number of comorbid conditions for any of the SNPs. Further analyses using genetic models (i.e., dominant or recessive) for each SNP yielded similar results. No significant relationship was found when the total number of risk alleles was entered as an independent variable in the multivariable models. When all multivariable models were also built for DHS and SHIP separately, the results within each study and the pooled dataset were similar. In order to control for potential RLS mimics, we repeated all analyses excluding subjects either having diabetes 112
Genetic Risk Loci for RLS and Multimorbidity—Szentkirályi et al.
0 risk allele
1 risk allele
2 risk alleles
Total Genotype
RLS Allele frequency (%)
Genotype
Allele frequency (%)
rs6747972
AA GA GG
1019 (19.8) 2548 (49.6) 1574 (30.6)
AA GA GG
108 (22.4) 244 (50.5) 131 (27.1)
rs6710341
AA GA GG
3594 (69.8) 1422 (27.6) 130 (2.5)
AA GA GG
335 (69.4) 140 (29) 8 (1.7)
rs11897119
TT CT CC
1904 (37) 2449 (47.6) 787 (15.3)
TT CT CC
192 (39.8) 226 (46.8) 65 (13.5)
rs12469063
GG GA AA
302 (5.9) 2001 (38.9) 2841 (55.2)
GG GA AA
23 (4.8) 191 (39.5) 269 (55.7)
rs2300478
GG TG TT
230 (4.9) 1833 (39) 2638 (56.1)
GG TG TT
13 (3) 171 (39.3) 251 (57.7)
rs3923809
AA GA GG
2485 (48.3) 2166 (42.1) 492 (9.6)
AA GA GG
266 (55.2) 185 (38.4) 31 (6.4)
rs9357271
TT CT CC
3104 (60.4) 1778 (34.6) 261 (5.1)
TT CT CC
315 (65.2) 146 (30.2) 22 (4.6)
rs6494696
GG CG CC
2350 (45.9) 2237 (43.6) 538 (10.5)
GG CG CC
239 (49.6) 206 (42.7) 37 (7.7)
rs11635424
GG GA AA
2352 (45.8) 2239 (43.6) 547 (10.6)
GG GA AA
236 (49) 208 (43.2) 38 (7.9)
rs12593813
GG GA AA
2376 (46.5) 2185 (42.8) 549 (10.7)
GG GA AA
241 (50.3) 200 (41.8) 38 (7.9)
rs1975197
AA GA GG
131 (2.5) 1322 (25.7) 3689 (71.7)
AA GA GG
15 (3.1) 123 (25.5) 345 (71.4)
rs4626664
AA GA GG
105 (2.1) 1309 (26.1) 3597 (71.8)
AA GA GG
12 (2.6) 134 (28.5) 324 (68.9)
rs3104767
GG TG TT
1761 (34.2) 2487 (48.3) 898 (17.5)
GG TG TT
180 (37.3) 231 (47.8) 72 (14.9)
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
Mean number of comorbid conditions
Mean number of comorbid conditions
Figure 1—The mean number of comorbid conditions according to the carrier status of each restless legs syndrome (RLS) risk allele in the pooled dataset of the Dortmund Health Study and the Study of Health in Pomerania. The left panel shows all subjects and subjects with RLS are represented by the right panel. Error bars represent standard error.
(n = 448) or reporting leg cramps (n = 380, assessed only in SHIP). These sub-analyses also yielded similar results. For the linear regression models in the total sample, power analyses suggested a statistical power ranging from 0.86 to 0.90 under conservative conditions. In models restricted to subjects with RLS, the predicted power ranged between 0.40 and 0.45.
Exploration of potential associations between risk variants and specific diseases may be informative, but such analyses were beyond the scope of the current work. We note that the set of SNPs based on our current limited knowledge was provided by genomewide association studies, and this selection may be biased. In the future, additional loci, especially rare variants with strong effects, might be found that contribute to both RLS and multimorbidity. The size of the pooled samples was sufficient to indicate a significant effect of the risk alleles on comorbidity. The results remained negative when the analyses were restricted to participants with RLS. Some of these subanalyses lacked sufficient power to indicate a statistically significant association and should be regarded as preliminary results. However, because no apparent trend was observed, it seems unlikely that a relationship between RLS risk variants and increased multimorbidity exists specifically in subjects with manifest RLS symptoms. Another limitation is that neurological examination has not been performed and potential mimics of RLS have not been thoroughly investigated. However, exclusion of subjects with diabetes or leg cramps did not modify the results. Nevertheless, other factors mimicking RLS may have led to some falsepositive cases. One strength of our analysis was the use of two independent cohort studies with participants randomly selected from the general population encompassing a broad age range.
DISCUSSION Based on the lack of association between RLS risk alleles and multimorbidity, it is unlikely that genetic susceptibility contributes substantially to the previously proposed relationship between multimorbidity and RLS.21 Generally, we have little knowledge about the exact role of the genes at these susceptibility loci, but their dysfunction may lead to several diseases. For example, the homeobox gene MEIS1 has a regulatory function in vascular development and its overexpression is important in leukemogenesis.41,42 SNPs within the MAP2K5 gene have been recently associated with obesity as well as anxiety and depression.43,44 There are some alternative explanations for the negative findings. Some of the involved genes may predispose to a specific disease listed in the comorbidity index; however, this effect may be counterbalanced by a reduced risk of another disorder. Furthermore, an increased risk for a less prevalent chronic disease may not be reflected in the comorbidity index. SLEEP, Vol. 39, No. 1, 2016
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Interviews were conducted by trained personnel resulting in high response rates. In summary, we did not find any significant association between carrier status of known RLS risk alleles and increased comorbidity in the general population. Further studies are required to explain the relationship between RLS and multimorbidity.
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ABBREVIATIONS ATC, Anatomic Therapeutic Chemical Classification BMI, body mass index BTBD9, BTB (POZ) domain-containing protein 9 CAPI, computer assisted face-to-face interview CES-D, Center for Epidemiologic Studies–Depression CID-S, Composite International Diagnostic-Screener DHS, Dortmund Health Study GFR, glomerular filtration rate HWE, Hardy-Weinberg equilibrium MAP2K5, mitogen-activated protein kinase kinase 5 MEIS1, myeloid ectopic viral integration site 1 PTPRD, protein tyrosine phosphatise, receptor type, D RLS, restless legs syndrome SHIP, Study of Health in Pomerania SKOR1, stelar K + outward rectifier SNP, single nucleotide polymorphism TOX3, TOX high mobility group box family member 3 REFERENCES 1. Gao X, Schwarzschild MA, Wang H, Ascherio A. Obesity and restless legs syndrome in men and women. Neurology 2009;72:1255–61. 2. Lopes LA, Lins Cde M, Adeodato VG, et al. Restless legs syndrome and quality of sleep in type 2 diabetes. Diabetes Care 2005;28:2633–6. 3. Winkelman JW, Shahar E, Sharief I, Gottlieb DJ. Association of restless legs syndrome and cardiovascular disease in the Sleep Heart Health Study. Neurology 2008;70:35–42. 4. Walters AS, Rye DB. Review of the relationship of restless legs syndrome and periodic limb movements in sleep to hypertension, heart disease, and stroke. Sleep 2009;32:589–97. 5. Ohayon MM, Roth T. Prevalence of restless legs syndrome and periodic limb movement disorder in the general population. J Psychosom Res 2002;53:547–54. 6. Lee HB, Hening WA, Allen RP, et al. Restless legs syndrome is associated with DSM-IV major depressive disorder and panic disorder in the community. J Neuropsychiatry Clin Neurosci 2008;20:101–5. 7. Winkelmann J, Prager M, Lieb R, et al. “Anxietas tibiarum”. Depression and anxiety disorders in patients with restless legs syndrome. J Neurol 2005;252:67–71. 8. Batool-Anwar S, Malhotra A, Forman J, Winkelman J, Li Y, Gao X. Restless legs syndrome and hypertension in middle-aged women. Hypertension 2011;58:791–6. 9. Lo Coco D, Mattaliano A, Lo Coco A, Randisi B. Increased frequency of restless legs syndrome in chronic obstructive pulmonary disease patients. Sleep Med 2009;10:572–6. 10. Taylor-Gjevre RM, Gjevre JA, Skomro R, Nair B. Restless legs syndrome in a rheumatoid arthritis patient cohort. J Clin Rheumatol 2009;15:12–5. 11. Pereira JC, Jr., Pradella-Hallinan M, Lins Pessoa H. Imbalance between thyroid hormones and the dopaminergic system might be central to the pathophysiology of restless legs syndrome: a hypothesis. Clinics (Sao Paulo) 2010;65:548–54. 12. Schurks M, Winter AC, Berger K, Buring JE, Kurth T. Migraine and restless legs syndrome in women. Cephalalgia 2012;32:382–9.
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SUBMISSION & CORRESPONDENCE INFORMATION
34. Wittchen HU, Hofler M, Gander F, et al. Screening for mental disorders: performance of the Composite International DiagnoticScreener (CID-S). Int J Methods Psychiatr Res 1999;8:59–70. 35. Khil L, Pfaffenrath V, Straube A, Evers S, Berger K. Incidence of migraine and tension-type headache in three different populations at risk within the German DMKG headache study. Cephalalgia 2012;32:328–36. 36. Pfaffenrath V, Fendrich K, Vennemann M, et al. Regional variations in the prevalence of migraine and tension-type headache applying the new IHS criteria: the German DMKG Headache Study. Cephalalgia 2009;29:48–57. 37. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. 38. Winkelmann J, Czamara D, Schormair B, et al. Genome-wide association study identifies novel restless legs syndrome susceptibility loci on 2p14 and 16q12.1. PLoS Genet 2011;7:e1002171. 39. Janowitz D, Schwahn C, Borchardt U, et al. Genetic, psychosocial and clinical factors associated with hippocampal volume in the general population. Transl Psychiatry 2014;4:e465. 40. Suhre K, Wallaschofski H, Raffler J, et al. A genome-wide association study of metabolic traits in human urine. Nat Genet 2011;43:565–9. 41. Azcoitia V, Aracil M, Martinez AC, Torres M. The homeodomain protein Meis1 is essential for definitive hematopoiesis and vascular patterning in the mouse embryo. Dev Biol 2005;280:307–20. 42. Lawrence HJ, Rozenfeld S, Cruz C, et al. Frequent co-expression of the HOXA9 and MEIS1 homeobox genes in human myeloid leukemias. Leukemia 1999;13:1993–9. 43. Rask-Andersen M, Jacobsson JA, Moschonis G, et al. The MAP2K5linked SNP rs2241423 is associated with BMI and obesity in two cohorts of Swedish and Greek children. BMC Med Genet 2012;13:36. 44. Jensen KP, Kranzler HR, Stein MB, Gelernter J. The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders. Am J Med Genet B Neuropsychiatr Genet 2014;165B:175–83.
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Submitted for publication March, 2015 Submitted in final revised form June, 2015 Accepted for publication July, 2015 Address correspondence to: András Szentkirályi, MD, PhD, Institute of Epidemiology and Social Medicine, University of Münster, AlbertSchweitzer-Campus 1, Building D3, D-48149, Germany; Tel.: +49-251-8358331; Fax: +49-251-83-55300; Email: [email protected]
DISCLOSURE STATEMENT Data collection in the Dortmund Health Study was supported by the German Migraine & Headache Society and by unrestricted grants of equal share from Almirall, Astra Zeneca, Berlin Chemie, Boehringer, Boots Health Care, GlaxoSmithKline, Janssen Cilag, McNeil Pharma, MSD Sharp & Dohme and Pfizer to the University of Muenster. SHIP is part of the Community Medicine Research Net of the University of Greifswald (available at http://medizin.uni-greifswfald.de/cm) and was funded by grant ZZ9603 from the Federal Ministry of Education and Research, Berlin, and the Ministers of Cultural and Social Affairs of the Federal State of Mecklenburg – West Pomerania, Schwerin.Studies were performed at the University of Münster (Dortmund Health Study) and the University of Greifswald (Study of Health in Pomerania). Dr. Winkelmann has received support from Xenoport for giving keynote speeches unrelated to the article. Dr. Berger has received research support for the conduction of the Dortmund Health Study as unrestricted grants of equal share from the German Migraine and Headache Society and a consortium formed by Allmiral, Astra-Zeneca, Berlin-Chemie, Boehringer Ingelheim Pharma, Boots Healthcare, GlaxoSmithKline, Janssen Cilag, McNeil Pharmaceuticals, MSD Sharp & Dohme, Pfizer. The other authors have indicated no financial conflicts of interest.
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http://dx.doi.org/10.5665/sleep.5328
NEUROLOGICAL DISORDERS
Lack of Association between Genetic Risk Loci for Restless Legs Syndrome and Multimorbidity András Szentkirályi, MD, PhD1 Henry Völzke, MD2,3; Wolfgang Hoffmann, MD, MPH2,4; Julianne Winkelmann, MD5,6,7,8; Klaus Berger, MD, MPH, MSc1 1 Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany; 2Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; 3German Centre for Cardiovascular Research, Partner site Greifswald, Germany; 4German Centre for Neurodegenerative Diseases (DZNE), Germany; 5Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; 6Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; 7Department of Neurology, Technische Universität München, Munich, Germany; 8Department of Neurology and Neurosciences, Stanford Center for Sleep Medicine and Sciences, Stanford University, Palo Alto, CA
Study Objectives: Multimorbidity is a risk factor for incident restless legs syndrome (RLS). In this relationship, the potential role of known genetic risk loci for RLS has not been studied. Our aim was to evaluate whether carriers of specific RLS risk alleles have higher comorbidity burden than noncarriers. Methods: The Dortmund Health Study (DHS) and the Study of Health in Pomerania (SHIP) are two independent cohort studies in Germany based on agestratified, random samples drawn from the respective population registers. DHS included 1,312 subjects and SHIP included 4,308 subjects. RLS status was assessed according to the RLS standard minimal criteria. A comorbidity index was calculated by summing the scores of the following conditions: diabetes, hypertension, myocardial infarction, obesity, stroke, cancer, renal disease, anemia, depression, thyroid disease, and migraine. Thirteen single nucleotide polymorphisms (SNP) previously associated with elevated risk of RLS were genotyped. Analyses were carried out on the pooled sample of the two studies. Results: The mean age was 50.4 ± 15.9 y, and the proportion of women was 51.4%. The mean number of comorbid conditions was 1.5 ± 1.3. In multivariable regression, the mean number of comorbidities was not significantly different between carriers of any of the RLS risk alleles and noncarriers either in the total pooled sample or in those having RLS symptoms. Conclusions: Based on these results it is unlikely that known genetic risk factors for RLS would lead to increased multimorbidity. Keywords: multimorbidity, restless legs syndrome, risk alleles Citation: Szentkirályi A, Völzke H, Hoffmann W, Winkelmann J, Berger K. Lack of association between genetic risk loci for restless legs syndrome and multimorbidity. SLEEP 2016;39(1):111–115. Significance Patients with RLS have an increased number of comorbid conditions in the general population. In order to examine whether genetic risk factors of RLS contribute to this relationship, we investigated the potential association between the presence of known genetic risk loci for RLS and higher comorbid burden in the general population. Since no significant relationship was detected, known RLS risk loci do not seem be risk factors of increased comorbidity seen in RLS patients. Further studies are required to clarify the background of the association between multimorbidity and RLS.
INTRODUCTION Many studies have reported on potential associations between restless legs syndrome (RLS) and different chronic diseases.1–20 According to our recent study, the simultaneous presence of several chronic conditions (multimorbidity) is an important risk factor for RLS.21 The etiology of the relationship between multimorbidity and RLS remains unclear. Variants in several loci encompassing the genes MEIS1, BTBD9, MAP2K5/SKOR1, PTPRD, and TOX3/noncoding ribonucleic acid have been recently identified by genome-wide association studies indicating a higher risk of RLS.22–25 We have limited knowledge about the exact function of the affected genes and their role in the development of RLS. We hypothesized that RLS risk variants may contribute to a greater comorbid burden. Therefore, we investigated whether the presence of RLS risk alleles is associated with an increased number of comorbid disease states in individuals from the general population and in those participants with RLS.
samples. The design, selection of participants, and data collection had been described previously.26,27 In brief, for DHS, the overall response at baseline was 66.9%. Assessment of RLS and co-morbidities were restricted to participants available for a face-to-face interview (n = 1,312). The final number of subjects participating in the SHIP was 4,308 (response 68.8%). All participants gave informed written consent, and the local ethics committees of the Medical Faculty at the University of Münster (for DHS) and the University Medicine of Greifswald (for SHIP) approved the study protocol.
METHODS
RLS Assessment RLS was assessed identically in both studies during face-toface interviews conducted by trained and certified interviewers with a short questionnaire that had previously been validated against physician classification 28 and already been used in prior reports.16,21,29,30 The questions followed the minimal criteria published by the International Restless Legs Syndrome Study Group.31 Participants were only classified as RLS positive if they answered all symptom questions with “Yes.”
Study Population The Dortmund Health Study (DHS) and the Study of Health in Pomerania (SHIP) are two independent population-based prospective cohort studies based on age-stratified random
Sociodemographic Data, Comorbidities, and Laboratory Measurements All data were assessed in computer-assisted face-to-face interviews (CAPI). The current medication, taken within the
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Thirteen candidate SNPs associated with elevated risk of RLS were selected on the basis of published data22–25 (Figure 1). In four cases, SNP call rate was below 92%: rs12593813 (89.5%) and rs12469063 (90.8%) in DHS and rs2300478 (84.5%) and rs4626664 (91.6%) in SHIP. The mean call rate for the other SNPs was 92.4 ± 0.17 and 94.7 ± 0.04 in DHS and SHIP, respectively. In the SHIP sample, the allele frequency of the SNP rs2300478 significantly deviated from the Hardy-Weinberg equilibrium (HWE) even after correction for multiple testing, whereas none of the other allele frequencies deviated significantly from HWE.
past seven days, was recorded and classified according to the Anatomic Therapeutic Chemical (ATC) Classification System (2006).32 The presence of diabetes, hypertension, myocardial infarction, stroke, and cancer was assessed as self-reports with specific questions asking for physician’s diagnosis of the respective condition. The phrasing of the questions and the answer categories were similar in the two studies. The classification of diabetes and hypertension was additionally based on the intake of medication. Prevalence of thyroid disease was based on medication (either thyreostatic or substitution therapy). The presence of depressive symptoms was assessed with the Center for Epidemiologic Studies–Depression (CES-D) scale33 in DHS and with the Munich-Composite International Diagnostic-Screener (CID-S)34 in SHIP. Migraine was assessed by a single question in SHIP and by a standardized questionnaire according to the International Headache Society’s new (2nd edition) classification criteria in DHS.35,36 Body weight and height were measured with shoes and heavy clothing removed. Body mass index (BMI) was calculated as mass (kg) divided by the square of height (m2). Obesity was defined as a BMI higher than 30 kg/m2. In SHIP and in a subset of DHS participants (n = 1,152) nonfasting blood samples were collected under standardized conditions for measurement of serum creatinine and hemoglobin. Renal function was estimated by glomerular filtration rate (GFR), which was calculated according to the Chronic Kidney Disease Epidemiology Collaboration formula.37 A GFR of 60 mL/min/1.73 m2 or less was considered as renal disease. Assessment of anemia was based on cutoffs for hemoglobin level (for women: 120 mg/dL; for men: 140 mg/dL) and/or medication against anemia (Anatomical Therapeutic Chemical classification “B03”). Comorbidity index as a measure of disease burden was calculated by determining the sum of the scores of the following conditions (diabetes, hypertension, myocardial infarction, obesity, stroke, cancer, renal disease, anemia, depression, thyroid disease, and migraine) following an established procedure.21
Statistical Analysis In order to increase statistical power, all analyses were performed on the pooled dataset of DHS and SHIP. To obtain normal distribution, the negative inverse of the square root of the comorbidity index was taken. Age- and sex-adjusted linear regression models were built separately for both studies to analyze the independent relationship between the number of risk alleles for each SNP and the comorbidity index. In these analyses, the presence of zero, one, or two risk alleles was entered as an independent categorical variable using zero risk alleles as the reference, whereas the comorbidity index was the dependent variable. When a recessive or dominant genetic model was used, the number of risk alleles was recoded accordingly to gain the respective dichotomized variable. The total number of risk alleles per individual was calculated by adding the number of risk alleles belonging to each of the 13 SNPs, thereby gaining a number ranging from 0 to 26. In each case two-tailed hypotheses were tested. All analyses were done with Stata 11.0 (StataCorp LP, College Station, TX, USA). Power calculations were performed for the multivariate linear regression models using the “powerreg” command. RESULTS Demographics and Basic Characteristics From a total of 5,620 available subjects, complete comorbidity data were available for 5,342 participants (DHS: n = 1,136, SHIP: n = 4,206). The mean age was 50.4 ± 15.9 y, and the proportion of women was 51.4%. The prevalence of RLS was 9.5% and the mean number of comorbid conditions was 1.5 ± 1.3. The allele frequencies for each SNP are shown in Figure 1.
Genotyping Details of genotyping and quality control have been published previously.38–40 Genomic deoxyribonucleic acid was prepared from fresh or frozen EDTA-anticoagulated blood samples. In DHS, genotyping was performed on the MassARRAY system using MALDI-TOF mass spectrometry with the iPLEX Gold chemistry (Sequenom Inc, San Diego, CA, USA), and primers were designed using MassArray AssayDesign 3.1.2.2 with iPLEX Gold default parameters. Automated genotype calling was done with SpectroTYPER 3.4 (Sequenom Inc, San Diego, CA, USA). In SHIP, 4,081 samples were successfully genotyped using the Affymetrix Genome-wide Human SNP Array 6.0 (Affymetrix, Santa Clara, CA, USA).39,40 The overall genotyping efficiency of the genomewide association was 98.6%. Genotypes were determined using the Birdseed2 clustering algorithm. Imputation of genotypes was performed with the software IMPUTE version 0.5.0 (Oxford, UK) against the HapMap II (CEU version 22, Build 36; International HapMap Consortium) reference panel using 869,224 genotyped SNPs. The total number of SNPs after imputation and quality control was 2,748,910. SLEEP, Vol. 39, No. 1, 2016
RLS Risk Alleles and Multimorbidity Figure 1 presents the mean number of comorbid conditions for each genetic risk variant for RLS in all subjects and in individuals with RLS symptoms. In multivariable linear regression models, the number of risk alleles was not associated significantly with the number of comorbid conditions for any of the SNPs. Further analyses using genetic models (i.e., dominant or recessive) for each SNP yielded similar results. No significant relationship was found when the total number of risk alleles was entered as an independent variable in the multivariable models. When all multivariable models were also built for DHS and SHIP separately, the results within each study and the pooled dataset were similar. In order to control for potential RLS mimics, we repeated all analyses excluding subjects either having diabetes 112
Genetic Risk Loci for RLS and Multimorbidity—Szentkirályi et al.
0 risk allele
1 risk allele
2 risk alleles
Total Genotype
RLS Allele frequency (%)
Genotype
Allele frequency (%)
rs6747972
AA GA GG
1019 (19.8) 2548 (49.6) 1574 (30.6)
AA GA GG
108 (22.4) 244 (50.5) 131 (27.1)
rs6710341
AA GA GG
3594 (69.8) 1422 (27.6) 130 (2.5)
AA GA GG
335 (69.4) 140 (29) 8 (1.7)
rs11897119
TT CT CC
1904 (37) 2449 (47.6) 787 (15.3)
TT CT CC
192 (39.8) 226 (46.8) 65 (13.5)
rs12469063
GG GA AA
302 (5.9) 2001 (38.9) 2841 (55.2)
GG GA AA
23 (4.8) 191 (39.5) 269 (55.7)
rs2300478
GG TG TT
230 (4.9) 1833 (39) 2638 (56.1)
GG TG TT
13 (3) 171 (39.3) 251 (57.7)
rs3923809
AA GA GG
2485 (48.3) 2166 (42.1) 492 (9.6)
AA GA GG
266 (55.2) 185 (38.4) 31 (6.4)
rs9357271
TT CT CC
3104 (60.4) 1778 (34.6) 261 (5.1)
TT CT CC
315 (65.2) 146 (30.2) 22 (4.6)
rs6494696
GG CG CC
2350 (45.9) 2237 (43.6) 538 (10.5)
GG CG CC
239 (49.6) 206 (42.7) 37 (7.7)
rs11635424
GG GA AA
2352 (45.8) 2239 (43.6) 547 (10.6)
GG GA AA
236 (49) 208 (43.2) 38 (7.9)
rs12593813
GG GA AA
2376 (46.5) 2185 (42.8) 549 (10.7)
GG GA AA
241 (50.3) 200 (41.8) 38 (7.9)
rs1975197
AA GA GG
131 (2.5) 1322 (25.7) 3689 (71.7)
AA GA GG
15 (3.1) 123 (25.5) 345 (71.4)
rs4626664
AA GA GG
105 (2.1) 1309 (26.1) 3597 (71.8)
AA GA GG
12 (2.6) 134 (28.5) 324 (68.9)
rs3104767
GG TG TT
1761 (34.2) 2487 (48.3) 898 (17.5)
GG TG TT
180 (37.3) 231 (47.8) 72 (14.9)
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0
Mean number of comorbid conditions
Mean number of comorbid conditions
Figure 1—The mean number of comorbid conditions according to the carrier status of each restless legs syndrome (RLS) risk allele in the pooled dataset of the Dortmund Health Study and the Study of Health in Pomerania. The left panel shows all subjects and subjects with RLS are represented by the right panel. Error bars represent standard error.
(n = 448) or reporting leg cramps (n = 380, assessed only in SHIP). These sub-analyses also yielded similar results. For the linear regression models in the total sample, power analyses suggested a statistical power ranging from 0.86 to 0.90 under conservative conditions. In models restricted to subjects with RLS, the predicted power ranged between 0.40 and 0.45.
Exploration of potential associations between risk variants and specific diseases may be informative, but such analyses were beyond the scope of the current work. We note that the set of SNPs based on our current limited knowledge was provided by genomewide association studies, and this selection may be biased. In the future, additional loci, especially rare variants with strong effects, might be found that contribute to both RLS and multimorbidity. The size of the pooled samples was sufficient to indicate a significant effect of the risk alleles on comorbidity. The results remained negative when the analyses were restricted to participants with RLS. Some of these subanalyses lacked sufficient power to indicate a statistically significant association and should be regarded as preliminary results. However, because no apparent trend was observed, it seems unlikely that a relationship between RLS risk variants and increased multimorbidity exists specifically in subjects with manifest RLS symptoms. Another limitation is that neurological examination has not been performed and potential mimics of RLS have not been thoroughly investigated. However, exclusion of subjects with diabetes or leg cramps did not modify the results. Nevertheless, other factors mimicking RLS may have led to some falsepositive cases. One strength of our analysis was the use of two independent cohort studies with participants randomly selected from the general population encompassing a broad age range.
DISCUSSION Based on the lack of association between RLS risk alleles and multimorbidity, it is unlikely that genetic susceptibility contributes substantially to the previously proposed relationship between multimorbidity and RLS.21 Generally, we have little knowledge about the exact role of the genes at these susceptibility loci, but their dysfunction may lead to several diseases. For example, the homeobox gene MEIS1 has a regulatory function in vascular development and its overexpression is important in leukemogenesis.41,42 SNPs within the MAP2K5 gene have been recently associated with obesity as well as anxiety and depression.43,44 There are some alternative explanations for the negative findings. Some of the involved genes may predispose to a specific disease listed in the comorbidity index; however, this effect may be counterbalanced by a reduced risk of another disorder. Furthermore, an increased risk for a less prevalent chronic disease may not be reflected in the comorbidity index. SLEEP, Vol. 39, No. 1, 2016
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Interviews were conducted by trained personnel resulting in high response rates. In summary, we did not find any significant association between carrier status of known RLS risk alleles and increased comorbidity in the general population. Further studies are required to explain the relationship between RLS and multimorbidity.
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ABBREVIATIONS ATC, Anatomic Therapeutic Chemical Classification BMI, body mass index BTBD9, BTB (POZ) domain-containing protein 9 CAPI, computer assisted face-to-face interview CES-D, Center for Epidemiologic Studies–Depression CID-S, Composite International Diagnostic-Screener DHS, Dortmund Health Study GFR, glomerular filtration rate HWE, Hardy-Weinberg equilibrium MAP2K5, mitogen-activated protein kinase kinase 5 MEIS1, myeloid ectopic viral integration site 1 PTPRD, protein tyrosine phosphatise, receptor type, D RLS, restless legs syndrome SHIP, Study of Health in Pomerania SKOR1, stelar K + outward rectifier SNP, single nucleotide polymorphism TOX3, TOX high mobility group box family member 3 REFERENCES 1. Gao X, Schwarzschild MA, Wang H, Ascherio A. Obesity and restless legs syndrome in men and women. Neurology 2009;72:1255–61. 2. Lopes LA, Lins Cde M, Adeodato VG, et al. Restless legs syndrome and quality of sleep in type 2 diabetes. Diabetes Care 2005;28:2633–6. 3. Winkelman JW, Shahar E, Sharief I, Gottlieb DJ. Association of restless legs syndrome and cardiovascular disease in the Sleep Heart Health Study. Neurology 2008;70:35–42. 4. Walters AS, Rye DB. Review of the relationship of restless legs syndrome and periodic limb movements in sleep to hypertension, heart disease, and stroke. Sleep 2009;32:589–97. 5. Ohayon MM, Roth T. Prevalence of restless legs syndrome and periodic limb movement disorder in the general population. J Psychosom Res 2002;53:547–54. 6. Lee HB, Hening WA, Allen RP, et al. Restless legs syndrome is associated with DSM-IV major depressive disorder and panic disorder in the community. J Neuropsychiatry Clin Neurosci 2008;20:101–5. 7. Winkelmann J, Prager M, Lieb R, et al. “Anxietas tibiarum”. Depression and anxiety disorders in patients with restless legs syndrome. J Neurol 2005;252:67–71. 8. Batool-Anwar S, Malhotra A, Forman J, Winkelman J, Li Y, Gao X. Restless legs syndrome and hypertension in middle-aged women. Hypertension 2011;58:791–6. 9. Lo Coco D, Mattaliano A, Lo Coco A, Randisi B. Increased frequency of restless legs syndrome in chronic obstructive pulmonary disease patients. Sleep Med 2009;10:572–6. 10. Taylor-Gjevre RM, Gjevre JA, Skomro R, Nair B. Restless legs syndrome in a rheumatoid arthritis patient cohort. J Clin Rheumatol 2009;15:12–5. 11. Pereira JC, Jr., Pradella-Hallinan M, Lins Pessoa H. Imbalance between thyroid hormones and the dopaminergic system might be central to the pathophysiology of restless legs syndrome: a hypothesis. Clinics (Sao Paulo) 2010;65:548–54. 12. Schurks M, Winter AC, Berger K, Buring JE, Kurth T. Migraine and restless legs syndrome in women. Cephalalgia 2012;32:382–9.
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Submitted for publication March, 2015 Submitted in final revised form June, 2015 Accepted for publication July, 2015 Address correspondence to: András Szentkirályi, MD, PhD, Institute of Epidemiology and Social Medicine, University of Münster, AlbertSchweitzer-Campus 1, Building D3, D-48149, Germany; Tel.: +49-251-8358331; Fax: +49-251-83-55300; Email: [email protected]
DISCLOSURE STATEMENT Data collection in the Dortmund Health Study was supported by the German Migraine & Headache Society and by unrestricted grants of equal share from Almirall, Astra Zeneca, Berlin Chemie, Boehringer, Boots Health Care, GlaxoSmithKline, Janssen Cilag, McNeil Pharma, MSD Sharp & Dohme and Pfizer to the University of Muenster. SHIP is part of the Community Medicine Research Net of the University of Greifswald (available at http://medizin.uni-greifswfald.de/cm) and was funded by grant ZZ9603 from the Federal Ministry of Education and Research, Berlin, and the Ministers of Cultural and Social Affairs of the Federal State of Mecklenburg – West Pomerania, Schwerin.Studies were performed at the University of Münster (Dortmund Health Study) and the University of Greifswald (Study of Health in Pomerania). Dr. Winkelmann has received support from Xenoport for giving keynote speeches unrelated to the article. Dr. Berger has received research support for the conduction of the Dortmund Health Study as unrestricted grants of equal share from the German Migraine and Headache Society and a consortium formed by Allmiral, Astra-Zeneca, Berlin-Chemie, Boehringer Ingelheim Pharma, Boots Healthcare, GlaxoSmithKline, Janssen Cilag, McNeil Pharmaceuticals, MSD Sharp & Dohme, Pfizer. The other authors have indicated no financial conflicts of interest.
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