Associations Between Dopamine Beta-Hydroxylase Gene Polymorphisms and Restless Legs Syndrome: A Case-Control Study

Introductıon

Restless legs syndrome (RLS), also known as Willis-Ekbom disease, is a chronic condition that causes an intense urge to move the legs at night, disrupting rest and sleep when they are most needed [1]. It is among the most prevalent neurological disorders. The estimated prevalence is age-dependent and varies from 9% to 14.2% in females and 5.4% to 9.4% in males [2–5]. RLS is a common condition that impacts sleep and significantly affects quality of life. The persistent discomfort and sleep disturbances associated with RLS can lead to fatigue, reduced cognitive function, and an overall decline in daily functioning, and in very severe cases, can be associated with increased risk of depression, suicide, and self-harm [6]. As a result, RLS often imposes a significant economic burden on healthcare systems, with many patients requiring long-term management and care. RLS is a significant clinical challenge and a major public health concern, necessitating greater awareness, improved diagnostic strategies, and more effective, accessible treatments to alleviate its broad impact on patients and healthcare systems.

Although the pathogenesis of RLS remains unknown, heritability estimates of 50–60% in twin and family studies early on indicated a significant impact of genetic factors on RLS susceptibility [4,7]. On the other hand, a growing literature indicates that the dopaminergic system may play a role in the development of this syndrome [8]. It makes sense to explore genes associated with dopamine transmission in RLS patients as potential factors influencing the risk of developing the condition. While significant progress has been made in understanding the genetic factors and the dopaminergic system’s role in RLS, several aspects of its pathophysiology remain unclear. A major gap in knowledge is the incomplete understanding of how genetic polymorphisms, such as those in the dopamine beta-hydroxylase (DBH) gene, influence the onset and severity of RLS.

Dopamine beta-hydroxylase (DBH) is an enzyme that limits the production of dopamine (DA) by converting it into norepinephrine. Initially identified in blood, DBH is a key enzyme in the biosynthesis of catecholamines and is crucial for neurotransmission within the adrenergic sympathetic nervous system [9]. A wealth of evidence shows that the characteristics of the DBH gene influence the enzyme’s activity in plasma [10].

Single-nucleotide polymorphisms (SNPs) in the DBH gene can alter the enzyme’s activity, leading to lower DA levels in the brain, which may contribute to the development of the disease. Furthermore, human DBH exhibits variability across different geographic locations or ethnic populations [11]. A promoter polymorphism – rs1611115 (C/T) – of DBH has been reported to alter gene expression. The T allele of the variant has been found to influence the promoter activity of DBH, resulting in reduced reporter gene expression [12]. The rs732833 DBH polymorphism is a specific SNP within the DBH gene, which is located on chromosome 9q34.2. This gene encodes the enzyme responsible for converting dopamine to norepinephrine [13]. The SNP rs129882 is located in the 3′-UTR of DBH. The C-allele of rs129882 was demonstrated to lead to reduced gene expression [14].

Recent studies have reported that the DBH gene SNPs rs129882, rs161115, and rs732833 are associated with neurological and psychiatric disorders such as Parkinson disease (PD), Alzheimer disease (AD), and attention deficit hyperactivity disorder (ADHD) [15–17]. However, the relationship between these DBH gene polymorphisms and RLS has not been investigated. To the best of our knowledge, this is the first study on this subject. The aim of our study was to evaluate the association between DBH gene SNPs (rs129882, rs161115, and rs732833) and genetic susceptibility to RLS in a Turkish population.

Materıal and Methods

STUDY DESIGN:

This case-control study included 103 Turkish patients with restless legs syndrome (RLS) and 100 healthy controls of similar age. The RLS patients were prospectively recruited from the Neurology Department at Inonu University between January 2022 and July 2023, and data were collected at the time of diagnosis before genotyping. All demographic, clinical, and genetic data were collected prospectively during the study period (January 2022 to July 2023). All patients and control participants provided written informed consent after receiving a detailed explanation of the study, in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Inonu University Faculty of Medicine (protocol number: 2022/02) and supported by the Scientific Research Projects Coordination Unit of Inonu University (Project code: TTU-3142). This ethics committee approval clearly and specifically covers the genetic testing and diagnostic procedures for RLS, including patient interviews, neurological examinations, and, if necessary, polysomnographic assessments.

CLINICAL ASSESSMENTS:

The diagnosis of RLS was made in accordance with the updated diagnostic criteria published by the International Restless Legs Syndrome Study Group (IRLSSG) in 2014. All patients fulfilled the 5 essential IRLSSG criteria: (A) an urge to move the legs, usually accompanied by uncomfortable sensations; (B) symptom onset or worsening during rest or inactivity; (C) partial or complete relief with movement; (D) symptoms worsening in the evening or night; and (E) exclusion of other conditions that could mimic RLS [4]. Face-to-face interviews and neurological examinations were conducted by experienced neurologists to confirm the diagnosis. In addition, the Turkish version of the validated Johns Hopkins telephone diagnostic questionnaire was used as a supportive tool [18]. RLS symptom severity was evaluated using the International Restless Legs Syndrome Study Group (IRLSSG) severity scale, which assesses symptom frequency, intensity, and impact on daily life and sleep. Scores were 1–10 (mild), 11–20 (moderate), 21–30 (severe), and 31–40 (very severe) [19].

EXCLUSION CRITERIA AND USE OF POLYSOMNOGRAPHY (PSG):

To ensure diagnostic precision and avoid confounding variables, individuals with known secondary causes of RLS such as renal failure, peripheral neuropathy, pregnancy, anemia, and rheumatoid arthritis were excluded. None of the participants reported use of medications that could affect sleep or sensory and motor function.

Importantly, the presence of other sleep disorders such as sleep apnea syndrome (SAS), REM sleep behavior disorder (RBD), narcolepsy, periodic limb movement disorder (PLMD), and idiopathic hypersomnia (IH) was carefully evaluated. All participants underwent a detailed clinical interview, and polysomnographic (PSG) evaluation was performed only for those whose medical history or interview responses suggested possible comorbid sleep disorders. A total of 16 patients were referred for overnight PSG, based on clinical suspicion.

Following PSG, 10 patients were excluded due to the presence of other sleep disorders: 2 patients were diagnosed with PLMD, 1 with narcolepsy, 5 with SAS, and 2 with IH. These exclusions were essential to isolate primary RLS cases and ensure the accuracy of phenotyping, as these disorders can mimic or influence RLS symptoms and interpretation.

All remaining RLS patients (n=103) met the IRLSSG 2014 criteria and reported RLS symptoms occurring at least 3 nights per week for more than 6 months.

DNA EXTRACTION AND GENOTYPING:

All subjects underwent venipuncture to extract 3 ml of fasting peripheral blood into a blood collection tube with EDTA-2Na anticoagulant at the time of their enrollment in the study. Genomic DNA was then extracted from whole-blood samples treated with EDTA using a High Pure PCR Template Preparation Kit (Roche Diagnostics GmbH, Mannheim, Germany), following the manufacture’s guidelines. The extracted DNA was stored at −20°C until further analysis. This process was carried out prospectively as part of the study protocol. Determination of DBH gene polymorphisms (rs129882, rs161115, and rs732833) was performed by Light Cycler real time PCR (Roche Diagnostics, Mannheim, Germany) using LightSNiP assay designed by TIB MOLBIOL (Berlin, Germany).

Each PCR mixture consisted of 10.4 μL of PCR-grade water, 1.0 μL of LightSNiP Reagent Mix, 2.0 μL of LCTM FastStart DNA Master HybProbe kit (Roche Diagnostics), 1.6 μL of MgCl2 (25 mM), and 5.0 μL of DNA solution. The mixture was placed into a capillary tube, centrifuged, and then loaded into the PCR machine. The PCR process involved an initial 10-minute denaturation step at 95°C, followed by 45 cycles: 10 seconds at 95°C for denaturation, 10 seconds at 60°C for annealing, and 15 seconds at 72°C for extension. The polymorphic, mutated, and wild-type alleles were identified based on the specific melting temperatures (Tm) of the resulting DNA fragments. All samples, both cases and controls, were genotyped in the same way.

STATISTICAL ANALYSIS:

All statistical analyses were performed using IBM^© SPSS^© Statistics version 25.0 (IBM Corporation, Armonk, NY, USA). Descriptive statistics are expressed as mean±standard deviation (minimum–maximum) for continuous variables and as frequency and percentage for categorical variables. The normality of data distribution was assessed using the Shapiro-Wilk test, histogram distribution, and skewness-kurtosis parameters. Homogeneity of variances was evaluated using Levene’s test for continuous variables. For categorical variables (sex, disease severity, family history, genotype, and allele distributions), the Pearson chi-square test and Fisher’s exact test were employed to evaluate proportions between group. For continuous variables showing normal distribution, age comparisons between RLS patients and controls were analyzed using the independent-samples t test. Age of onset and disease duration comparisons among genotype groups (CC, CT, TT) for rs1611115, rs129882, and rs732833 polymorphisms were analyzed using one-way ANOVA. Hardy-Weinberg equilibrium (HWE) was assessed in the control group for each polymorphism using the Pearson chi-square test. A P value >0.05 indicated that the population was in Hardy-Weinberg equilibrium for the respective polymorphism, ensuring absence of population stratification and random mating assumptions. Logistic regression analysis was performed to identify independent predictors of RLS risk using the Enter method. Odds ratios (OR) with 95% confidence intervals (CI) were calculated for significant predictors. Covariates with P<0.20 in univariate analysis were entered into a backward multivariate model. A two-tailed P value <0.05 was considered statistically significant for all analyses.

Results

BASELINE CHARACTERISTICS:

A total of 103 RLS patients and 100 healthy individuals participated in the study. Among the 103 patients, 70 were female (68%) and 33 were males (32%), with an average age of 54.41±15.56 years. The control group consisted of 49 females (49%) and 51 males (51%), with an average age of 50.08±17.80 years. According to clinical characteristics, mild disease severity was the most common category in the RLS group (43.7%). The mean age at onset was 50.25±15.02 years, and the mean disease duration was 49.42±60.31 months. Family history was negative in 95.1% of the patients. No significant differences were observed between the 2 groups in terms of age distribution (P=0.066), while the proportion of females was significantly higher in the patient group than in the control group (P=0.007). Table 1 summarizes the basic demographic data of study population and clinical data of the patients.

HARDY-WEINBERG EQUILIBRIUM:

The genotype distributions of DBH rs129882, rs732833, and rs1611115 polymorphisms in the control group conformed to HWE (P=0.941, P=0.931, and P=0.904, respectively), which indicated our subjects formed a representative group (Table 2).

GENOTYPE AND ALLELE FREQUENCY DISTRIBUTION:

The genotype and allele distributions of DBH rs129882, rs732833, and rs1611115 SNPs are shown in Table 3. The CC, CT, and TT genotype frequencies of DBH rs732833 were 37.9%, 55.3%, and 6.8% in the case group and 43%, 39%, and 18%, respectively, in the control group, and the homozygous TT and CT genotype frequency was significantly different between the 2 groups (P<0.05). No significant difference was detected between allele frequencies of rs732833 (P>0.05). The genotype and allele distributions of rs129882 and rs1611115 between the 2 groups were not significantly different (P>0.05).

INDEPENDENT PREDICTORS OF RLS:

After the identification of potentially significant risk factors in the univariate analysis, a multivariate logistic regression analysis was conducted to evaluate the relationship between RLS and the SNPs and clinical factors by adjusting for significant variables. In multivariate logistic regression analysis, female sex (odds ratio=2.238; 95% confidence interval (CI): 1.225–4089; p=0.009) and rs 732833 CT genotype (odds ratio=0.515; 95% confidence interval (CI): 0.272–0.974; p=0.041) were significant and independent predictor of RLS (Table 4).

CLINICAL CHARACTERISTICS OF RLS PATIENTS IN DBH POLYMORPHISMS:

A separate analysis was conducted for each of the 3 polymorphisms based on clinical characteristics of RLS, including sex, age at disease onset, disease duration, disease severity (mild, moderate, severe, very severe), and positive family history. For rs1611115 DBH, no significant differences were observed among the CC, CT, and TT genotypes in terms of sex, age at disease onset, disease duration, disease severity, and family history. Allele frequency analysis showed that the C allele was significantly more common than the T allele in the mild severity group (P=0.008) (Table 5). The genotype and allele distributions of rs129882 and rs732833 DBH polymorphisms did not differ significantly according to sex, age at disease onset, disease duration (months), disease severity, or family history (Tables 6, 7).

Discussion

To our knowledge, this is the first study to compare the DBH SNPs (rs129882, rs732833, rs1611115) in Turkish patients with RLS and age-matched healthy controls, and to explore the connection between these SNPs and factors such as RLS severity, disease duration, family history, and age at onset.

The underlying mechanisms of RLS are not completely understood. RLS is a complex condition in which genetic background, environmental factors, and gene–environment interactions predispose people to disease and affect expression of the full clinical phenotype [20]. Dopamine metabolism is the most frequently discussed mechanism in the pathophysiology of disease. In RLS, total dopaminergic activity is increased, resulting in the downregulation of dopamine receptors. There is a circadian profile of dopamine activity in RLS, with hyperfunctioning during the morning and throughout the day, followed by relative hyperfunctioning in the evening and nighttime [21]. Iron deficiency is also one of the most common comorbid conditions with RLS and is of key importance given the role of iron deficiency in RLS pathophysiology and the potential for specific treatment [22]. Numerous studies have indicated a high prevalence of RLS symptoms in individuals with conditions associated with insufficient iron availability [23]. Most patients with RLS do not have systemic iron deficiency but rather have brain iron deficiency. Brain iron deficiency in RLS can result from alterations of iron acquisition by the brain [24].

Altered glutamatergic neurotransmission is also known to play a role in the pathogenesis of RLS. Animal studies have identified hypersensitivity of corticostriatal glutamatergic terminals, which may be associated with RLS symptoms [25], and increased basal glutamate levels in the thalamus have been observed in RLS patients compared to the control group. Additionally, treatments that are effective in alleviating RLS symptoms influence glutamate receptors or glutamate release [26]. Furthermore, studies in rodents have demonstrated that brain iron deficiency significantly downregulates adenosine A1 receptors (A1R) while upregulating striatal A2A receptors (A2AR), ultimately leading to increased sensitivity of corticostriatal glutamatergic terminals [27]. Beyond their effects on glutamate release, A1R and A2AR interact with dopamine D1 receptors (D1R) and D2 receptors (D2R), forming A1R-D1R and A2AR-D2R heteromers, respectively. These heteromers are highly expressed in striatonigral and striatopallidal neurons and can exert complex inhibitory modulation of dopaminergic signaling, ultimately disrupting the adenosine–dopamine–glutamate balance in the striatum [28]. Therefore, the mechanisms involved in the pathophysiology of RLS appear to be functionally interconnected.

The relationship between locus coeruleus (LC) hyperactivity and sleep disturbances, including motor restlessness, has been shown in recent studies [29]. LC dysfunction has been linked to conditions like RLS, where hyperactivity of the noradrenergic system might contribute to the motor restlessness commonly observed in RLS patients. For instance, a study emphasized how stress-induced LC activity increases can contribute to sleep disruptions, potentially influencing conditions like RLS that involve both sleep disturbances and motor restlessness [30]. These findings indicate a potential link between LC dysfunction and disorders like RLS, offering pathways for further research into therapeutic targets for managing both sleep-related issues and motor symptoms. Additionally, enhanced noradrenergic activity originating from the LC may exert regulatory effects on the functional activity of dopaminergic neurons [31]. In traditional studies, up to 60% of individuals with RLS reported having a family history of the condition [32]. Therefore, genetic factors also play a role in the development of RLS.

The DBH is responsible for maintaining synaptic dopamine reservoir and dopamine turnover in neurons and catalyzes the oxidative hydroxylation of dopamine to noradrenalin [10]. DBH is an enzyme required to synthesize norepinephrine from dopamine in noradrenergic cells of the LC and post-ganglionic sympathetic fibers, as well as in adrenal medulla chromaffin cells and epinephrine neurons [33]. The enzymatic activity of DBH is characterized by wide interindividual variation regulated by genetic inheritance [10,34]. These association study results are not surprising given the vital role of DBH in the catecholaminergic signaling pathway and how widespread norepinephrine is throughout the body. Therefore, elucidating the functional relevance of SNPs in DBH remains very important.

The DBH gene (OMIM 223360, NM_000787) is located on chromosome 9q34. The DBH gene is composed of 12 exons, coding for 603 amino-acid proteins and comprises a sequence of approximately 23 kb [35,36]. DBH exhibits considerable genetic variability and influences DBH activity, serving as a key quantitative trait locus that regulates enzyme function in both serum and cerebrospinal fluid [11,37,38]. The important role of DBH in the catecholaminergic pathway, along with LC dysfunction and the role of noradrenergic system hyperactivity in RLS, suggests that DBH polymorphisms contributes to RLS pathophysiology not only through the dopaminergic system but also by increasing LC activity and influencing catecholaminergic pathways. Additionally, changes in dopamine levels may indirectly affect the adenosine–dopamine–glutamate balance, which plays a crucial role in RLS pathophysiology.

The only study in the literature investigating the association between DBH gene SNP and RLS was conducted by Desautels et al, focusing on a single-nucleotide polymorphism involved in dopamine catabolism; however, no significant findings were reported [39].

The SNP rs1611115, an upstream promoter element, is one of the most-studied polymorphisms in the DBH gene [11,40]. This promoter polymorphism is strongly associated with plasma DBH (plDBH), and accounts for up to 52% of the variation in plDBH activity across populations of different geographic origins [11,41]. The T allele in homozygous conditions was found to be associated with very low plDBH [42].

The association of the rs1611115 DBH polymorphism with various neurological and psychiatric diseases has been investigated in numerous studies in the literature [13,43–47]. A study of Alzheimer disease patients found that the TT variant of the DBH rs1611115 polymorphism interacts with environmental factors, leading to increased cognitive impairment [46]. In the study by Hashmi et al, the minor allele T (−1021C>T) of the rs1611115 polymorphism was found to be significantly associated with an increased risk of bipolar disorder and schizophrenia [47]. There are conflicting results in the literature concerning the association between the rs1611115 DBH polymorphism and movement disorders. While Shao et al reported that the TT genotype increases the risk of PD by 2.95 times and that the C allele increases the risk of PD onset [13], Healy et al reported that the T/T genotype was under-represented among PD patients, suggesting a protective effect against the disease [40]. Other studies in the literature have suggested that the rs1611115 SNP can delay the onset age of PD [43,48]. Another study conducted in India reported no association between rs1611115 polymorphisms and PD, and found no significant difference in plasma DBH activity between patients and healthy controls [49]. Similarly, a study conducted on patients with Wilson disease did not identify a significant association with this polymorphism [50]. Unlike previous studies, which have identified associations between the rs1611115 polymorphism and the T allele in various diseases, our study found a stronger association between the C allele and mild RLS severity. While the T allele is thought to influence dopamine metabolism, our findings suggest that the genetic basis of RLS is multifactorial, involving not only dopaminergic pathways but also other neurotransmitter systems, such as glutamate and adenosine. This new perspective on the pathophysiology of RLS highlights the possibility that the C allele functions differently from the T allele in modulating RLS severity.

The SNP rs129882 is mapped to the 3′ UTR region of the DBH gene and is located 3960 bp upstream of the DBH antisense RNA 1 (DBH-AS1). It is thought that the allelic change from C to T in rs129882 DBH activates the transcription of DBH-AS1, which subsequently affects the expression of DBH, reducing/suppressing its expression levels [51]. rs129882 DBH has been researched in attention deficit hyperactivity disorder (ADHD), PD and Wilson disease [49,50,52]. Although no significant effects were found in Wilson disease [50], the rs129882 DBH polymorphism has been found to increase the risk of Parkinson disease [13]. Additionally, it has been suggested that reduced DBH gene expression increases the risk of ADHD by affecting the conversion of dopamine to norepinephrine [14]. In our study, no significant association was found between the rs129882 DBH polymorphism and RLS.

The SNP rs732833 is a specific single-nucleotide polymorphism within the DBH gene, located on chromosome 9q34.2. It has been linked to variations in plasma DBH activity, which can affect the dopaminergic and noradrenergic systems. In the literature, rs732833 has been investigated only in relation to PD, and no significant association was found in PD patients of Han ethnicity [13]. In contrast, our study revealed that the frequencies of the rs732833 DBH CT and homozygous TT genotypes were significantly higher in RLS patients compared to the control group. This polymorphism may increase the risk of RLS by enhancing DBH activity, leading to reduced dopamine levels, and may also influence glutamatergic and adenosinergic pathways. To our knowledge, the present study is the first to evaluate the association between the SNP rs732833 and RLS.

The present study explored the effect of rs1611115, rs129882, and rs732833 DBH on RLS for the first time, and in Turkish RLS patients, the rs732833 DBH CT and TT genotypes were found to be significantly different from the healthy control group, and the C allele of the rs1611115 DBH is more associated with mild severity compared to the T allele. Our results therefore contribute to the literature in this respect.

Our study has several limitations. It focused on a population of Turkish individuals from eastern Turkey. Further research with larger sample sizes is needed to explore the association of rs1611115, rs129882, and rs732833 DBH polymorphisms with RLS in other populations. However, in our study, the DBH gene polymorphisms rs129882, rs732833, and rs1611115 were found to be in accordance with Hardy-Weinberg equilibrium (P=0.931, 0.904, and 0.941, respectively). Conformity with Hardy-Weinberg equilibrium indicates that the examined genetic variants are randomly and homogeneously distributed within the population. This finding supports the representativeness of our sample from a genetic standpoint and suggests that potential biases did not influence the outcomes. Furthermore, data consistent with genetic equilibrium imply that the results are generalizable to other populations and that certain SNPs may have potential as clinical biomarkers. Therefore, adherence to Hardy-Weinberg equilibrium enhances the validity and reliability of our genetic analysis findings. Another significant limitation of the present study is the low prevalence of reported familial history of RLS among the participants (95%), which may undermine the strength of the observed genotype-phenotype associations. This suggests that the majority of cases in this cohort are idiopathic rather than hereditary, potentially restricting the generalizability of our findings to genetically predisposed populations. Future investigations focusing on individuals with a documented family history of RLS could offer deeper insights into the role of genetic factors in the pathophysiology of the disorder.

Conclusions

We found that the rs732833 DBH polymorphism predisposes individuals to RLS, and mild disease severity is more commonly associated with the C allele of the rs1611115 DBH than with the T allele. To the best our knowledge, this Turkish study is the first to investigate the relationship between RLS and DBH gene polymorphisms. Further studies will be required to confirm these findings and to understand the mechanisms through which these SNPs might influence the development of RLS.