Exploring the Top 50 Drugs Associated with Restless Legs Syndrome Based on the FDA Data from 2004 to 2024

Introduction

Restless legs syndrome (RLS; also known as Willis–Ekbom disease) is a common neuro-sensory disorder characterized by an intense urge to move the legs, particularly when at rest or before sleep. This condition worsens during the night and at rest, significantly impacting individuals’ quality of life and mental health.¹ Over the years, RLS has been a common yet often undiagnosed condition, frequently underestimated. The prevalence of RLS among individuals aged 20 to 79 worldwide is approximately 7.12%, with a higher rate in women (8.27%) compared to men (5.98%). Most studies show that RLS prevalence increases with age, peaking around 60 years. Factors such as being female, older age, depression, and metabolic disorders (like diabetes and iron deficiency) increase the risk of developing RLS.²

The exact mechanisms underlying RLS remain unclear, although brain iron deficiency is widely recognized as a pathological mechanism.³ However, an increasing body of research indicates that disruptions in dopamine levels, as well as changes in the glutamatergic and adenosinergic systems, are linked to the pathophysiology of RLS.^4–6 Genetic factors are also associated with RLS, as a significant proportion of patients report a family history of the condition. Twin studies have shown that identical twins exhibit a higher concordance rate for RLS compared to fraternal twins,⁷ over the past few decades, several large genome-wide association studies have identified more than 20 risk loci, with MEIS1 confirmed as the strongest genetic risk factor for RLS.^8–10 However, the genetic architecture of RLS remains elusive due to factors such as population stratification, sample selection, and statistical analysis methods.

In addition to its primary associations with conditions such as renal failure (specifically end-stage renal disease necessitating hemodialysis), iron deficiency, and pregnancy, RLS may also be linked to the use of specific medications.¹¹ Medications can potentially trigger the onset of RLS or exacerbate symptoms in individuals already diagnosed with the condition. However, current research on the pathogenesis of drug-associated RLS is still in its preliminary stages and has not been fully elucidated. Recent evidence suggests that it may be associated with pharmacological effects on multiple receptor systems and neurotransmitter pathways.¹² Common classes of medications that can induce RLS include antipsychotics, antidepressants, and anticonvulsants.¹³ Additionally, the development of drug-associated RLS is influenced by individual sensitivity, medication dosage, concurrent use of multiple drugs, and other risk factors such as age, smoking, and gastrointestinal disorders.¹⁴

The latest treatment guidelines recommend the use of α2δ ligand medications (gabapentin, gabapentin enacarbil, and pregabalin) for RLS in adults, rather than the dopamine agonists (pramipexole, ropinirole, and rotigotine) that have been widely used in clinical practice. Although dopaminergic therapy is initially highly effective, it can lead to acute adverse reactions (such as nausea, vomiting, and somnolence), as well as a worsening of symptoms over time with long-term use, known as augmentation.¹⁵ Augmentation refers to the progressive exacerbation of the intensity and duration of RLS symptoms, which develops over a period of months to years following exposure to dopaminergic agents. This phenomenon is characterized by one or more of the following manifestations: an earlier onset of symptoms compared to the period before dopaminergic treatment (for example, shifting from nighttime to daytime), a decreased latency to symptom onset during sedentary activities, and/or the extension of symptoms to additional areas of the body.

To our knowledge, most existing literature on drug-associated RLS consists of case reports and reviews, with no comprehensive systematic studies evaluating the association between drugs and RLS in The Food and Drug Administration Adverse Event Reporting System (FAERS). Therefore, we conducted this study focusing on the pharmacological classification of drugs known to associated with RLS, while also aiming to identify high-risk medications for RLS that have not yet been clearly linked to the condition, thereby providing references for the safety of clinical drug use. FAERS is the largest adverse event reporting database globally, recording individual case safety reports (ICSRs), and has been utilized in numerous high-quality studies.^16,17 It provides extensive data to identify drug-related risk signals and offers critical information for regulatory decisions made by clinicians. This study encounters certain limitations. While limitations in the completeness of the ICSRs, the inability to establish a definitive causal relationship or differentiate between exacerbation or new onset RLS limit the analysis of FAERS, it provides extensive data to identify drug-related risk signals to identify a list of drugs associated with RLS that may be useful to clinicians.

Methods

Data Sources

Data from the first quarter of 2004 to the third quarter of 2024 was downloaded from FAERS (FAERS Quarterly Data Extract Files). Each file contains seven types of data: DEMO (patient demographics), REAC (all MedDRA terms encoded for events), DRUG (drug/biologic information), OUTC (patient outcomes), RPSR (report source), THER (start and end dates of drug therapy), and INDI (indications for drug use).

Identification of Target Data

In FAERS, adverse events (AEs) are standardized using the Preferred Terms (PT) from the MedDRA 25.0. We searched the PT column for “restless legs syndrome” (MedDRA code: 10058920) to identify drugs associated with RLS and downloaded all relevant reports. Following FDA guidelines, we removed duplicate data. If cases had the same CASEID, we retained the most recent report based on FDA_DT; if both CASEID and FDA_DT were identical, we kept the report with the larger PRIMARYID. After the initial removal of duplicates, some PRIMARYID duplicates were still found, leading to a secondary round of duplicate removal.

Statistical Analysis and Risk Signal Detection

In this study, we utilized R software (version 4.3.2, RStudio) for data processing and analysis. First, we conducted descriptive analyses to summarize the clinical characteristics of patients with drug-associated RLS, including gender, weight, age, and reporting country. Next, we listed the 50 most common drugs associated with reported RLS events and classified them according to the Anatomical Therapeutic Chemical (ATC) classification system (https://www.who.int/tools/atc-ddd-toolkit/atc-classification). To identify potential risk signals for RLS, we employed four methods: Reporting Odds Ratio (ROR),¹⁸ Proportional Reporting Ratio (PRR),¹⁹ Bayesian Confidence Propagation Neural Network (BCPNN),²⁰ and Empirical Bayesian Geometric Mean (EBGM).¹⁹ All four methods were calculated based on a 2×2 contingency table, with specific calculation criteria detailed in Table 1. To minimize bias, reduce the false positive rate, and strengthen the detection threshold, we required that all four methods meet the criteria simultaneously to confirm a valid risk signal. This comprehensive approach enhanced the accuracy and reliability of risk signal detection, providing robust data support for subsequent drug safety assessments and regulatory decisions.

Table 1 Four Major Algorithms Used for Risk Signal Detection

We extracted FAERS reports containing patient information (gender, age, weight) and analyzed only those with complete ICSRs. For drugs that generated valid signals and met the criterion a>40, we conducted univariate and multivariate logistic regression analyses using RStudio to identify the presence of risk factors associated with drug-associated RLS, p-value of < 0.05 was considered statistically significant.

In the final stage of this study, we analyzed the onset time of adverse reactions associated with drugs that generated significant risk signals. The time to onset (TTO) of adverse reactions was defined as the time interval between the date of the adverse event (EVENT_DT) and the date of drug use initiation (START_DT). To ensure the accuracy of calculations involving reports with date input errors (ie, where EVENT_DT precedes START_DT), we excluded entries with inaccurate dates and specific data omissions, we used the median, interquartile range, and Weibull shape parameter (WSP) tests to evaluate TTO. The Weibull distribution is characterized by two parameters: the scale parameter (α) and the shape parameter (β).²¹ The scale parameter dictates the distribution’s scale or width, whereas the shape parameter influences the configuration of the distribution curve. In the analysis of TTO, the shape parameter was employed to predict the hazard of AEs over time, classifying outcomes into early, random, or wear-out failure. A shape parameter and its 95% CI both less than 1 indicate a decreasing risk (early failure), values approximately equal to 1 with a CI encompassing 1 suggest a constant risk (random failure), and values greater than 1 with a CI exceeding 1 imply an increasing risk (wear-out failure).

Result

Basic Characteristics of RLS-Related ADE Reports

The entire research process is illustrated in Figure 1. Data from the FAERS database was retrieved, resulting in 53,463,446 adverse drug events (ADEs), of which 16,410 (0.03%) were associated with drug-associated RLS. As shown in Figure 2, the highest number of reports occurred in 2019, with 1212 cases, and there was an overall upward trend in reports from 2004 to 2024. A total of 15,935 patients were included in this study, the clinical characteristics of these patients are detailed in Table 2. Reports from females (9902 reports, 62.1%) were significantly higher than those from males (4375 reports, 27.5%), the most affected age group was individuals aged 41 and older (7470 reports, 46.9%). The majority of reports were submitted by consumers (8388 reports, 52.6%), with the United States being the primary reporting country (10,859 reports, 68.1%). Hospitalization was the most common clinical outcome (2878 reports, 18.1%).

Table 2 Clinical Characteristics of Patients with Drug-Associated RLS (From 2004Q1 to the 2024Q3)

Figure 1 Flow chart for identification of RLS reports of suspected ADEs.

Figure 2 Distribution of baseline data for subjects with drug-associated RLS in the FAERS database. Notes: (A) shows the age distribution pyramid chart of subjects with drug-associated RLS; (B) shows the distribution of subjects with drug-associated RLS by reporting year; (C) shows the distribution of adverse reaction outcomes for subjects with drug-associated RLS; (D) shows the distribution of medication intake methods for subjects with drug-associated RLS; (E) shows the distribution of reporting countries for subjects with drug-associated RLS.

The 50 Most Common Drugs

Based on the count of adverse drug reaction reports related to RLS, we identified the top 50 drugs (Figure 3), which had a total of 16,410 reports. Below is a categorical description of these drugs, with a particular focus on the ten most common ones, including their report counts and percentages: sodium oxybate (648 cases, 3.95%), metoclopramide (492 cases, 3.00%), pramipexole (360 cases, 2.19%), quetiapine fumarate (345 cases, 2.10%), mirtazapine (338 cases, 2.06%), aripiprazole (332 cases, 2.02%), pregabalin (320 cases, 1.95%), ropinirole (316 cases, 1.92%), adalimumab (313 cases, 1.91%), and alendronate sodium (255 cases, 1.55%). We categorized the drugs and found that those related to the nervous system constituted a significant proportion of RLS-related ADEs, with sodium oxybate (648 cases) and pramipexole (360 cases) being the most reported drugs in this category, In the category of alimentary tract and metabolism, metoclopramide (492 cases) had the highest number of reports, among antineoplastic and immunomodulating agents, adalimumab (313 cases) and interferon beta-1a (213 cases) were also reported frequently, in the musculoskeletal system category, burosumab (247 cases) and alendronate sodium (255 cases) had a relatively high number of reports, in the category of systemic hormonal preparations, excluding sex hormones and insulins, teriparatide (177 cases) was the most reported drug. Additionally, other categories such as the cardiovascular system, dermatologicals, and blood and blood-forming organs were also represented in RLS-related ADEs, but their counts were relatively low.

Figure 3 Top 50 drugs with the highest frequency of reported drug-associated RLS.

Risk Signal Detection

We employed four methods to calculate the signal values for the 50 most common drugs. According to the ROR criteria, Table 3 lists the top 50 drugs with the strongest risk signals. Out of the 50 drugs, 27 generated risk signals, with the top five results as follows: burosumab [ROR (95% CI): 61.16 (53.88, 69.43)], ropinirole [ROR (95% CI): 60.19 (53.80, 67.35)], pramipexole [ROR (95% CI): 44.29 (39.87, 49.20)], rotigotine [ROR (95% CI): 30.77 (26.88, 35.23)], and metoclopramide [ROR (95% CI): 26.73 (24.43, 29.25)]. Among the drugs evaluated that met all algorithm criteria, 27 were identified as producing valid risk signals, of the 27 drugs, only six FDA-approved drugs (such as burosumab, pramipexole, rotigotine, quetiapine fumarate, enzalutamide, and duloxetine) had labels that documented the expected RLS reactions. Notably, 21 of the 27 drugs did not indicate any RLS risk in their labels.

Table 3 Risk Signal Scores for Drug-Associated RLS

Regression Analysis

The correlation between patient gender, age, weight, and medication use with drug-associated RLS is shown in Figure 4. During the examination of these 27 drugs, it was found that ICSRs for burosumab and brexpiprazole were incomplete, resulting in their exclusion. Univariate and multivariate logistic regression analyses indicated that gender, age, weight, and medication could be influencing factors for drug-associated RLS (p < 0.05), the ROC-AUC indicating the model’s predictive accuracy was 0.72 (Figure 5). Specifically, compared to the age group under 44, the incidence risk decreased for those over 57, in contrast, higher weight groups had an increased risk compared to those under 64 kg, and males had a lower incidence risk than females. Regarding medication use, 24 drugs, excluding buprenorphine, were identified as independent risk factors for drug-associated RLS. Notably, the medications metoclopramide, pramipexole, rotigotine, mirtazapine, and ropinirole hydrochloride had odds ratios (OR) of 53.09 (95% CI: 44.09, 63.92), 35.94 (95% CI: 29.50, 43.79), 22.29 (95% CI: 18.19, 27.32), 18.70 (95% CI: 15.55, 22.48), and 18.64 (95% CI: 14.72, 23.61), with p-values all significantly less than 0.001.

Figure 4 Results of the multi-factor logistic regression analysis.

Figure 5 The ROC curves of drug-associated RLS risk factors. Abbreviations: ROC, receiver operating characteristic; AUC, area under curve.

Time-to-Onset Analysis

A TTO analysis was conducted on the 27 drugs that generated risk signals, excluding brexpiprazole, diphenhydramine hydrochloride/ibuprofen due to incomplete AE time data. The results are presented in Table 4. In the WSP analysis assessment, the shape parameters β for naltrexone and asenapine maleate were close to 1, with the 95% CI including 1, indicating a random failure type. For the remaining 23 drugs, all shape parameters β and their 95% CI upper limits were found to be < 1. This suggests that the incidence of drug-associated RLS decreases over time, indicating the presence of early failure types.

Table 4 Analysis of RLS Time-to-Onset for 25 Drugs Generating Risk Signals

Discussion

This study is the first to use real-world data from the FAERS database to comprehensively assess the correlation between RLS and clinical drug use, summarizing patient clinical characteristics and identifying medications closely associated with RLS. The findings indicate that reported cases have been increasing annually since 2004, stabilizing after approximately 15 years. Furthermore, although RLS is generally considered a non-fatal condition, the incidence of severe outcomes, such as death and life-threatening events, was 1.6% and 2.1%, respectively, in the analyzed cases. Warranting attention, the observed increased risk of mortality may be attributed to potential suicidal and self-harming behaviors.²² We observed significant gender and age differences in all drug-associated RLS cases reported to the FDA, with RLS being more common in females. Multivariate regression results indicate that female patients have a higher risk of onset compared to males, this phenomenon may be attributed to the elevated incidence rate of RLS observed in women. In this study, approximately 46.9% of drug-associated RLS cases occurred in individuals aged 41 and older. Although systematic studies investigating the characteristics of the drug-associated RLS population are lacking, a systematic review and modeling study indicated that globally, among patient with RLS aged 20–79, females account for 57.89%, the prevalence of RLS begins to increase after age 18, peaking around age 60,² which aligns with the characteristics observed in our study. Currently, the age-related risk of drug-associated RLS is unclear. Our study found that the risk of occurrence is lower in the >44 age group compared to those <44 (OR < 1), advanced age may serve as a protective factor against drug-associated RLS.

Regarding weight, individuals >64 kg exhibited a higher risk of onset compared to those <64 kg (OR > 1), higher weight may be a risk factor for drug-associated RLS. Given obesity is associated with OSA and OSA can exacerbate RLS,^23,24 it is possible that OSA may be a factor in increased prevalence of drug associated RLS with higher weight.²⁵ Certainly, further clinical trials are needed to elucidate potential confounding factors and to determine whether gender, age, and weight are risk factors for drug-associated RLS.

Preliminary evidence from case reports have documented drug-associated RLS associated with medications such as antidepressants, antipsychotics, anticonvulsants, opioid analgesics, antihistamines, and antiemetics.^2,26–30 Population-level studies remain scarce, a case-control study involving dialysis patients indicated that antidepressants, antipsychotics, antihistamines, and antiemetics significantly increased the risk of developing RLS, with odds ratios ranging from 1.47 to 2.28 (all p < 0.0001),³¹ but the evidence linking these medications to RLS remains limited, and there is a risk of false positives. Therefore, the limited generalizability of prior evidence, predominantly comprising case reports and single-population studies, underscores the critical need for our FAERS-based analytical framework.

In this study, we identified drugs associated with RLS that have been systematically reported in the FAERS database. Among the 50 most commonly reported drugs, those affecting the nervous system were the most prevalent (25/50), followed by antineoplastic and immunomodulating agents (11/50) and musculoskeletal system drugs (5/50). The following 27 drugs were identified as valid risk signals: burosumab, ropinirole hydrochloride, pregabalin, rotigotine, metoclopramide, diphenhydramine hydrochloride/ibuprofen, asenapine maleate, mirtazapine, lurasidone, sodium oxybate, valbenazine tosylate, quetiapine fumarate, brexpiprazole, aripiprazole, naltrexone, enzalutamide, sertraline, carbidopa/levodopa, venlafaxine, duloxetine, gabapentin, fluoxetine, buprenorphine, alendronate sodium, pregabalin, citalopram, and rosuvastatin calcium (Table 3), these 27 drugs were categorized primarily as antidepressants (mirtazapine, venlafaxine, duloxetine, fluoxetine, citalopram), followed by antipsychotics (asenapine, lurasidone, quetiapine fumarate, brexpiprazole, aripiprazole), antiparkinson agents (ropinirole, pramipexole, rotigotine, carbidopa/levodopa), antiepileptics (gabapentin, pregabalin), and other drug types (including antiemetics, analgesics, and monoclonal antibodies).

The relationship between antidepressants and RLS has been explored in numerous case reports, cross-sectional studies, and open-label trials, but the findings have been inconsistent.^32–34 Our study supports the notion that antidepressants are a risk factor for drug-associated RLS and are the most commonly reported drug category, noradrenergic and specific serotonergic antidepressants (NaSSAs), such as mirtazapine, as well as selective serotonin reuptake inhibitors (SSRIs) like fluoxetine and citalopram, and serotonin-norepinephrine reuptake inhibitors (SNRIs), including venlafaxine and duloxetine. Evidence from case reports and cross-sectional studies indicates that antidepressants may induce or exacerbate RLS,^33,35 which may be associated with the frequent occurrence of comorbid depression or a depressive state among patients with RLS. As demonstrated in a recent meta-analysis, the pooled prevalence of depression or depressive states among RLS patients reaches as high as 30.39% (95% CI: 20.55–42.43%).³⁶ Therefore, we recommend seeking medications with a lower risk profile, reducing dosages, or substituting drugs that may trigger or worsen RLS. Furthermore, the mechanisms by which antidepressants may lead to RLS remain unclear. A clinical study by Jhoo et al compared the availability of the serotonin transporter (SERT) between 16 untreated patient with RLS and 16 healthy controls, finding that the severity of RLS symptoms increased as the availability of SERT decreased. They further suggested that increased serotonergic transmission may be associated with the exacerbation of RLS symptoms.³⁷

Among the antipsychotics we identified, the five drugs (asenapine, lurasidone, quetiapine fumarate, brexpiprazole, and aripiprazole) have numerous case reports, except for brexpiprazole,^38–41 quetiapine fumarate is the most frequently reported drug associated with RLS, the five drugs are classified as second-generation antipsychotics. The mechanisms of action of antipsychotic medications predominantly involve the inhibition of dopaminergic neurotransmission, in addition to engaging multiple other pathways. Dopaminergic inhibition might clarify the connection between antipsychotics and RLS, as shown in previous studies. Furthermore, a meta-analysis indicated that patients administered antipsychotic medications exhibited reduced iron levels. This finding implies, from an alternative standpoint, that antipsychotic drugs may contribute to the onset of RLS through their impact on iron metabolism.⁴² There are case reports indicating that RLS induced by antipsychotic drugs can be resolved by changing the medication. For example, olanzapine-induced RLS completely resolved after changing olanzapine to aripiprazole, risperidone, and quetiapine.⁴³ Additionally, a genome-wide association study involving 190 schizophrenia patients further indicated that a haplotype of the MAP2K5 gene polymorphism may increase susceptibility to RLS symptoms induced by antipsychotics.⁴⁴ Given that the presence and exacerbation of drug-associated RLS symptoms may depend on the dosage of antipsychotics,⁴⁵ it is advisable to switch to drugs with a lower risk or to adjust the dosage for RLS induced by antipsychotics.

Among the four antiparkinson agents identified in this study, ropinirole, pramipexole, and rotigotine are all dopamine receptor agonists. Carbidopa/levodopa is a combination formulation, with levodopa serving as a precursor to dopamine, while carbidopa is a peripheral dopa decarboxylase inhibitor. Antiparkinson agents are also used to treat RLS due to their ability to enhance dopaminergic effects. A cross-sectional cohort study determined that the annual incidence of RLS augmentation during the initial eight years of dopamine agonist therapy was 8%. Within this cohort, the risk of augmentation was most pronounced with levodopa, as compared to ropinirole and pramipexole, potentially attributable to levodopa’s relatively shorter half-life,^1,46 patients receiving the long-acting dopamine agonist rotigotine may have a lower risk of augmentation (approximately 4% of patients).⁴⁷ This suggests that the concept of reduced dopaminergic function as the origin of RLS mechanisms is incomplete. The notion of a “hyper-dopaminergic” presynaptic state, defined by enhanced dopamine synthesis, release, and reduced uptake, resulting in elevated synaptic dopamine levels, is increasingly attracting scholarly attention. The augmentation of RLS symptoms may be attributable to prolonged dopaminergic therapy, which induces a hyperdopaminergic state. It is crucial to recognize that this state is frequently associated with a “hypo-dopaminergic” postsynaptic state, characterized by reduced D2/3 receptor availability.^48,49

Among the antiepileptics identified as risk factors for drug-associated RLS, gabapentin and pregabalin are classified as α2δ calcium channel ligands, α2δ ligands are believed to reduce postsynaptic excitability and limit the release of several calcium-mediated neurotransmitters,⁵⁰ their efficacy in RLS may stem from their ability to counteract the excessive excitatory state associated with RLS. The latest international guidelines strongly recommend the use of α2δ calcium channel ligands for the treatment of adults with RLS, unless contraindicated.¹⁵ In a 52-week randomized double-blind trial, the augmentation rate for pregabalin was significantly lower than that for pramipexole (2.1% vs 7.7%, P=0.001),⁵¹ another single head-to-head study confirmed this conclusion.⁵²

As a specific complication of long-term dopaminergic treatment, it is necessary to clarify the distinction between augmentation and other forms of symptom worsening. Augmentation refers specifically to the long-term iatrogenic increase in the intensity and duration of RLS symptoms that gradually occurs after months to years of dopaminergic therapy. This phenomenon is characterized by an earlier onset of symptoms (for example, shifting from nighttime to daytime), a shortened latency of symptoms during periods of inactivity, and the extension of symptoms to other parts of the body. In contrast, other forms of worsening, such as acute adverse reactions that are common at the beginning of treatment (nausea, vomiting, drowsiness), as well as worsening factors for RLS symptoms other than antidopaminergic medications (eg, alcohol, caffeine, antihistamines, serotonergic medications), and the natural progression of RLS symptoms that are unrelated to medication effects, do not exhibit these specific temporal or dose-dependent characteristics.¹⁵ Recognizing this distinction is crucial, as augmentation often necessitates a reassessment of treatment strategies (eg, dosage adjustment or switching to other medications), while transient adverse reactions or disease progression may require different management approaches.

Sodium oxybate is the most frequently reported drug-associated RLS in this study, it is a sodium salt of gamma-hydroxybutyrate (GHB), acting on several neurotransmitters, including gamma-aminobutyric acid (GABA). GHB has a complex impact on the dopaminergic system, influencing dopamine release by bidirectionally acting on GABAergic neurons.⁵³ Glutamate is a crucial excitatory neurotransmitter in the central nervous system, and besides dopaminergic system, a hyperglutamatergic state is also involved in the development of RLS.⁵⁴ Surprisingly, GHB can modulate glutamate levels in the hippocampus in a concentration-dependent manner by acting on GHB receptors.⁵⁵

Burosumab is the drug with the highest risk signal for adverse reactions in this study, which merits attention to some extent, it is approved for the treatment of patients with X-linked hypophosphatemia and tumor-induced osteomalacia, although there are no studies reporting that burosumab induces RLS, it is mentioned in its package insert. Future research should delve deeper into the connection between the two.

Among the remaining drugs, metoclopramide is a dopamine receptor antagonist, some case reports indicate that administration of metoclopramide can lead to transient RLS symptoms, which resolve after discontinuation of the medication.³⁰

Buprenorphine and naltrexone are opioids used to treat patients with opioid use disorder, a survey of 129 patients with opioid use disorder found that, 13.2% of those receiving buprenorphine or naltrexone maintenance treatment exhibited RLS symptoms, occurring at least 5 to 15 times per month,^56,57 previous case-control studies have found that 51% of patients undergoing opioid withdrawal treatment have RLS, significantly higher than the 21.7% in the alcohol withdrawal group.⁵⁸ These studies suggest that opioid withdrawal may be more likely to trigger RLS. A prospective observational study further explored the incidence and clinical features of RLS during opioid withdrawal, as well as the intervention effects of buprenorphine and pregabalin.⁵⁹ The results showed that buprenorphine was ineffective in alleviating RLS, while pregabalin had good effects. However, a retrospective cohort study indicated that buprenorphine significantly reduced RLS symptoms,⁶⁰ effectively eliminated patients’ need for dopamine agonists, and improved sleep and other quality of life measures. This highlights the need for larger-scale, randomized controlled trials to comprehensively assess whether buprenorphine can serve as an alternative therapy for refractory RLS. RLS symptoms persisted even after opioid withdrawal symptoms subsided, suggesting that RLS may be an independent disease that may not be related to opioid withdrawal, or it could have preexisted. Some have proposed that endogenous and exogenous opioids protect against the loss of dopaminergic neurons, which may explain the potential association between opioid withdrawal and RLS.⁶¹

Enzalutamide inhibits androgen receptor activity, and most studies linking it to drug-associated RLS are case reports,^62–64 given that dopamine signaling is strongly regulated by androgens in various brain regions,⁶⁵ we hypothesize that androgen deficiency may lead to dopaminergic impairment.^66–68

Diphenhydramine hydrochloride (commonly referred to as DPH) is a first-generation antagonist of the histamine H1 receptor, exhibiting notable anticholinergic properties. This drug category is frequently encountered among patients with RLS. Antihistamines are prevalent in over-the-counter (OTC) sleep aids, such as diphenhydramine and doxylamine, as well as in OTC cold medications. They are often combined with ibuprofen, which makes their presence even less obvious.⁶⁹ A case-control study conducted utilizing the US Renal Data System (USRDS) examined the relationship between the use of specific medications, including antihistamines, and the prevalence of RLS in patients with end-stage renal disease. The findings indicated that antihistamines were among the medication classes significantly correlated with an elevated likelihood of RLS diagnosis [OR(95% CI): 60.19 (1.79, 2.10), p < 0.0001].³¹ An animal study offered an alternative perspective by investigating the relationship between antihistamines and RLS using histidine decarboxylase knockout mice.⁷⁰ While no significant alterations in sleep architecture were detected, these knockout mice demonstrated a decrease in tibialis anterior electromyographic (EMG) bursts during non-rapid eye movement (NREM) sleep compared to their wild-type counterparts. Crucially, these findings do not corroborate the hypothesis that inhibiting brain histamine signaling may facilitate RLS. Rather, the results indicate that limb movements during sleep, including those occurring at brief intervals, are indicative of subcortical arousal that necessitates intact brain histamine signaling.

Given the ubiquitous presence of diphenhydramine in OTC aids and combination cold medications, patients often encounter diphenhydramine without clinical supervision. Therefore, clinicians should exercise caution when recommending or prescribing OTC medications containing diphenhydramine, especially for patients with existing RLS or those at risk of developing RLS. The evidence provided in this article is one of the formal records documenting the relationship between diphenhydramine use and RLS, filling an important gap in clinical observations.

Valbenazine, alendronate sodium, and rosuvastatin calcium are also identified as potential risk factors for drug-associated RLS. However, the lack of discussion on their correlation with RLS in current medical literature, the level of evidence supporting these associations is considered low. Consequently, these factors will not be examined in detail in this discussion.

Additionally, we analyzed the TTO of drug-associated RLS. We observed that the median TTO for naltrexone was 2 days, making it the shortest among all the studied drugs. In contrast, the median TTO for metoclopramide was 1467.5 days, the longest among all analyzed drugs. Furthermore, of the 25 drugs, 18 had a median onset time of less than 100 days, indicating that drug-associated RLS onset occurs early in the course of treatment with these drugs. However, there is currently a lack of data analysis regarding the TTO of drug-associated RLS.

When exploring the association between drugs and RLS in the FAERS database, we encounter several limitations. (1) As a spontaneous reporting system, the integrity and accuracy of FAERS data are constrained by the voluntary nature of reporting, which may lead to underreporting, duplicate reports, and inaccuracies, ultimately affecting the reliability of the research findings. (2) The lack of information about healthy populations in the FAERS database prevents us from accurately calculating the incidence of drug-associated RLS, and we are unable to assess potential differences among various populations. (3) The correlation between RLS and medications is based on descriptions in the reports rather than rigorous causal assessments, making it difficult to ascertain whether these risk signals reflect true causal relationships. The occurrence of RLS may be influenced by underlying patient conditions, concomitant medications, or other unknown factors. (4) the FAERS database does not include information on patients’ health status prior to medication use and only contains data on drug indications. Therefore, we cannot determine whether patients had RLS before taking the medication, making it impossible to distinguish between drug-induced RLS and drug-exacerbated RLS. Finally, our findings require further validation through prospective studies and clinical trials to confirm the causal relationship between drugs and RLS. Despite these limitations, the FAERS database remains a crucial tool for monitoring drug safety and identifying potential risk signals.

Conclusion

This study utilized FAERS data to compile a comprehensive list of drugs potentially associated with RLS. We extracted reports from the FAERS database from the first quarter of 2004 to the third quarter of 2024, identifying 50 drugs most commonly associated with RLS and calculating their respective signal values. Notably, only six drugs (burosumab, pramipexole, rotigotine, quetiapine fumarate, enzalutamide, and duloxetine) listed RLS as an adverse effect in their prescribing information. Individuals under 44 years of age, weighing more than 64 kg, female individuals, or those who have taken one of 24 specific medications are at a higher risk of developing drug-associated RLS. Time-to-onset analysis revealed distinct patterns for the main associated drugs, with most exhibiting early onset of adverse reactions (within 100 days). Our list may assist clinicians in identifying alternative medications to consider when managing patients at risk for early RLS. All findings reported here require further validation through additional clinical studies and animal experiments.