Abstract

Introduction: Restless legs syndrome (RLS) is a frequent yet underrecognized comorbidity in rheumatoid arthritis (RA), potentially driven by inflammation-mediated disturbances in iron metabolism.

Aim: This study investigated the relationship between inflammatory markers and iron homeostasis in RA patients with and without RLS.

Methods: This monocentric cross-sectional study included 32 RA patients, 12 of whom had RLS and 20 who did not. RLS was diagnosed using ICSD-3 criteria, and severity was measured using IRLSSG scores. Clinical data, disease activity (DAS28-CRP), reported sleep parameters, and laboratory markers (IL-6, TNF-α, serum iron, ferritin, and hepcidin-25) were analyzed. Between-group comparisons and receiver operating characteristic (ROC) analyses were performed.

Results: The prevalence of RLS in our cohort was 37.5%. Compared to RA patients without RLS, those with RLS demonstrated significantly prolonged reported sleep latency (42.5 vs. 25.0 min, p<0.001, δ=0.86); higher disease activity (DAS28-CRP 4.64 vs. 3.82, p=0.0029, δ=0.64); elevated IL-6 (13.55 vs. 2.84 pg/mL, p=0.0022, δ=0.66); and hepcidin-25 levels (640 vs. 455 pg/mL, p=0.0382, δ=0.48) and lower serum iron (15.35 vs. 19.30 µmol/L, p=0.0292, δ=−0.47). Ferritin and TNF-α did not differ significantly. ROC analysis showed strong discrimination for reported sleep latency (AUC 0.93), IL-6 (AUC 0.83), and DAS28-CRP (AUC 0.82).

Conclusions: RA patients with RLS demonstrate a distinct biological profile characterized by heightened inflammatory activity and functional iron deficiency. The IL-6–hepcidin–iron axis emerges as a central mechanistic pathway linking systemic inflammation to RLS pathophysiology. These findings support the concept that the magnitude of inflammatory activation, rather than its mere presence, contributes to RLS development in RA and highlight potential targets for improved diagnostic and therapeutic strategies.

Keywords

restless legs syndrome, rheumatoid arthritis, interleukin-6, hepcidin, iron metabolism, inflammation

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects up to 1% of adults worldwide.^[1] Besides progressive joint destruction, RA is accompanied by systemic complications and comorbidities such as depression, cardiovascular disease, anemia, and sleep disturbances.^[2] The latter are not trivial complaints—population-based cohorts show that RA patients have a significantly higher risk of sleep disorders than age- and sex-matched controls.^[3,4] Although studies show considerable methodological heterogeneity, it is persistently reported that at least one-third of RA patients have sleep problems.^[3] Sleep impairment exacerbates systemic inflammation and pain, contributes to fatigue, depression, and disability, and worsens the quality of life.^[5] This initiates a vicious, self-reinforcing cycle that drives progressive disease exacerbation and may impair effective therapeutic control.^[6,7] The timely recognition of disturbed sleep is therefore critical for an effective and holistic approach to RA.^[5,7]

Among sleep disorders, restless legs syndrome (RLS) is particularly relevant.^[4,8,9] RLS is a neurological sensorimotor disorder characterized by an urge to move the legs associated with unpleasant sensations, which worsen at rest or at night and cause distress and disrupted sleep.^[10] In the general population the prevalence is 5%–10%.^[11] In rheumatoid arthritis, the burden is much higher.

Meta-analyses confirm the trend—pooled data show an RLS prevalence of 26.6% in RA.^[12]. Nonetheless, most rheumatology studies focus on pain and disease activity or other major and well-recognized comorbidities, and RLS remains overlooked. Because RLS sensations can mimic arthritic pain, misdiagnosis may lead to inadequately addressed issues or unnecessary medication changes. Moreover, early detection and treatment of RLS in rheumatic diseases improves quality of life.^[9,13-15]

The pathophysiological basis for increased prevalence of RLS in RA has a strong theoretical foundation. It has been repeatedly shown that RLS pathogenesis is fundamentally intertwined with iron dysmetabolism.^[16] Specifically, it is the impaired iron compartmentalization in the central nervous system rather than an absolute systemic iron deficiency that is more closely associated with the neurotransmitter signaling abnormalities in RLS.^[17] In RA patients, pathological processes exist that increase the vulnerability to iron mishandling by the responsible systems. RA is recognized as a prototypical autoimmune disease marked by chronic inflammation, which predisposes to anemia of chronic disease.^[18] Chronic inflammation in RA upregulates IL-6^[19], which in turn stimulates hepcidin production^[20,21], resulting in iron sequestration, which may precipitate RLS in susceptible individuals^[13]. Furthermore, IL-6 is a key mediator of both RA pathogenesis and sleep regulation.^[22,23] While RLS is increasingly recognized as a frequent comorbidity of RA, few studies have investigated the specific biological pathways linking RA-associated inflammation, iron metabolism, and RLS. Building on evidence that hepcidin is altered^[24-26] in idiopathic RLS and that IL-6 drives hepcidin expression and anemia in RA^[20,27], we hypothesized that RA patients with RLS would exhibit altered iron metabolism in relation to systemic inflammation. Demonstrating such differences would provide mechanistic insight into RLS in RA. To our knowledge, no previous research has compared hepcidin, IL-6, and iron status in RA patients with and without RLS.

Aim

The aim of this study was to investigate the association of inflammatory and iron metabolism biomarkers with the presence of RLS in patients with RA.

Materials and methods

This is a monocentric cross-sectional observational study. During primary or routine clinic visits, patients underwent a comprehensive examination by an experienced rheumatologist, including assessment of disease activity using the Disease Activity Score 28–C-reactive protein (DAS28–CRP) score. Inclusion criteria for patients in the study were an age between 18 and 70 years and patients diagnosed with rheumatoid arthritis, classified according to the 2010 ACR/EULAR classification criteria. The exclusion criteria were severe anemia (Hb<80 g/L); heart failure class II, III, or IV according to the New York Heart Association (NYHA) functional classification or heart failure with reduced ejection fraction; chronic kidney disease stage >IIIb; liver cirrhosis; Parkinsonian syndrome or Parkinson’s disease; stroke in the acute or subacute phase or prior stroke with residual deficits (modified Rankin scale, mRS≥2); treatment with dopamine agonists, antipsychotics, or lithium; and pregnancy. Forty-three newly diagnosed patients and patients with established diagnoses of RA were considered for screening. However, only 32 matched the criteria and signed the informed consent. Among these, 12 patients fulfilled criteria for restless legs syndrome and 20 served as controls without RLS. Demographic data (age and sex), body‑mass index (BMI), and disease duration were extracted from the clinical records. Clinical laboratory testing like complete blood count with differential, rheumatoid factor, serum iron, ferritin, C-reactive protein, and erythrocyte sedimentation rate was performed locally in the clinic’s laboratory.

All procedures performed in this study were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments.

Evaluation of restless legs syndrome and sleep variables

RLS was diagnosed using the International Classification of Sleep Disorders, 3rd edition^[28] and severity was assessed with the International Restless Legs Syndrome Study Group (IRLSSG) criteria and questionnaire. Patients were interviewed about the presence of an urge to move the legs, symptom onset or worsening during rest, relief with movement, and circadian variation, and other potential secondary causes were excluded. During application of the RLS diagnostic criteria, the clinical interview incorporated a structured and exhaustive differential diagnostic framework to systematically exclude established RLS mimics. RLS severity was quantified with the IRLSSG rating scale (scores 0–40). Daytime sleepiness was assessed by the Epworth Sleepiness Scale (ESS). All participants reported their usual subjective sleep duration (hours/night) and time to fall asleep.

Measurement of hepcidin and selected cytokines

Fasting venous blood samples were collected between 07:00 and 09:00 h after an overnight fast. Serum concentrations of IL-6 and TNF-α were measured using an automated chemiluminescent immunoassay (CLIA) on the MAGLUMI platform (Snibe, China), following the manufacturer’s protocol. The assay is based on a two-step sandwich principle using magnetic microbeads coated with monoclonal antibodies and ABEI-labeled detection antibodies, with signal intensity directly proportional to analyte concentration. Serum hepcidin-25 levels were determined using a sandwich enzyme-linked immunosorbent assay (ELISA) kit (EH3222, FineTest, Wuhan, China). The assay employs immobilized capture antibodies and enzyme-labeled detection antibodies, generating a colorimetric signal proportional to hepcidin concentration. Hepcidin-25 was successfully quantified in 27 patients. Samples from 5 patients were excluded due to insufficient serum volume for reliable measurement. We measured hepcidin-25 in the cohort of RA patients since it is the biologically active form of hepcidin. This avoids the interpretation of concentrations of hepcidin involving degradation products.^[29]

Statistical analysis

Continuous variables are expressed as mean ± standard deviation or median (interquartile range) when non‑normally distributed. Categorical variables are expressed as frequencies and percentages. Differences between RA patients with and without RLS were analyzed using the independent‑sample t test or Mann–Whitney U test for continuous variables and the chi‑square test for categorical variables. Effect size was estimated with Cliff’s delta. To explore discriminatory performance, receiver operating characteristic (ROC) analysis was performed for selected variables. A two‑sided p‑value <0.05 was considered statistically significant. All analyses were performed using SPSS version 27 (IBM Corp.).

Results

The dataset comprised 32 patients with rheumatoid arthritis. Twelve patients (37.5%) met the criteria for RLS and 20 patients (62.5%) did not. The continuous variables were summarized as median and interquartile range (IQR, Q1–Q3) (Table 1).

Table 1.

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Anthropometrics, disease activity of RA, clinical laboratory data and subjective sleep parameters compared between rheumatoid arthritis patients with RLS (RLS+) and without RLS (RLS−)

Parameter	RLS + Median	RLS + IQR	RLS − Median	RLS − IQR	p-value
Age (years)	63.50	7.75	60.50	12.25	0.861
BMI (kg.m⁻²)	24.05	2.83	25.39	8.39	0.340
DAS28-CRP	4.65	0.94	3.82	1.10	0.0029*
CRP (mg/l)	2.98	5.23	1.65	2.20	0.186
Leukocytes (×10⁹/l)	6.13	2.90	7.05	1.70	0.695
Erythrocytes (×10¹²/l)	4.19	0.61	4.42	0.78	0.340
Hemoglobin(g/l)	127.5	21.25	127.0	16.25	0.626
MCV (fL)	89.4	4.03	88.6	8.78	0.654
RDW (%)	13.6	1.80	13.95	2.55	0.697
ESR (mm/h)	30.0	30.0	32.5	40.0	0.507
Creatinine (µmol/l)	58.2	17.55	59.3	12.10	0.938
Fasting glucose (mmol/l)	4.41	1.00	4.92	0.71	0.228
Serum iron (µmol/l)	15.35	6.90	19.30	8.45	0.029*
Ferritin (ng/ml)	88.1	61.58	69.1	75.88	0.800
Urea (mmol/l)	4.81	1.05	4.93	1.55	0.508
RF (UI/ml)	11.4	29.08	11.45	42.95	0.922
IL-6 (pg/ml)	13.55	24.07	2.85	2.94	0.0022*
TNF-α (pg/ml)	3.75	7.98	3.82	5.15	0.654
Hepcidin-25 (pg/ml)	640.0	225.35	455.0	245.0	0.038*
ESS (points)	4.50	4.00	4.00	3.25	0.477
Reported total sleep (h)	6.00	0.38	6.50	1.00	0.073
Reported time to sleep (min)	42.50	10.00	25.00	11.25	>0.001*

Clinical and laboratory comparison between groups

Patients with restless legs syndrome (RLS) had substantially longer subjective sleep latency than patients without RLS. The median time taken to fall asleep was 42.5 minutes (interquartile range (IQR) 40.0–50.0) in the RLS group, compared to 25.0 minutes (IQR 18.8–30.0) in the non-RLS group (p=0.00005; Cliff’s delta=0.86). Disease activity was also higher in patients with RLS. DAS28-CRP score was higher in the RLS group (4.64, IQR 4.51–5.45) than in the non-RLS group (3.82, IQR 3.08–4.17; p=0.0029; Cliff’s delta =0.64) (Fig. 1). Serum iron was lower in the RLS group (15.35, IQR 9.46–16.35) than in the non-RLS group (19.30, IQR 14.78–23.23; p=0.0292) (Fig. 2). Hepcidin was higher in the RLS group (640.0, IQR 507.5–732.9) than in the non-RLS group (455.0, IQR 373.8–618.8; p=0.0382) (Fig. 3). By contrast, ferritin levels did not differ significantly between the two groups (p=0.800). Inflammatory cytokine profiling revealed a significant increase in IL-6 levels in the RLS group (13.55, IQR 3.83–27.90) compared to the non-RLS group (2.84, IQR 1.69–4.63; p=0.0022; Cliff’s delta =0.66) (Fig. 4). TNF-α did not differ significantly between the two groups (p=0.654).

Figure 1.

Comparison of disease activity (DAS28-CRP) in patients with and without restless legs syndrome.

Figure 2.

Comparison of serum iron in patients with and without restless legs syndrome.

Figure 3.

Comparison of hepcidin-25 is increased in RLS group compared to Non-RLS.

Figure 4.

Comparison of IL-6 in patients with RLS compared to Non-RLS RA patients.

Most routine hematologic and biochemical indices, including age, body mass index, CRP, leukocyte count, erythrocyte count, hemoglobin, MCV, ESR, creatinine, fasting glucose, ferritin, RF, and urea, did not show statistically significant between-group differences. A trend toward shorter subjective total sleep duration was observed in the RLS group (6 h vs. 6.30 h; p=0.073) (Table 1).

Effect-size analysis

Effect-size analysis confirmed that the main between-group differences were not merely driven by p-values. Sleep latency showed a large effect (Cliff’s delta=0.86). IL-6 (delta=0.66) and DAS28 (delta=0.64) showed medium-to-large effects. Serum iron (delta=−0.47) and hepcidin (delta=0.48) showed moderate effects.

ROC analysis and candidate thresholds

ROC analysis was used to estimate the discriminatory ability of the main variables and to derive exploratory cut points using the Youden index. These thresholds should be interpreted as hypothesis-generating only and not as clinically validated diagnostic cutoffs.

The strongest discriminator was reported sleep latency (time to sleep), with an AUC of 0.93 and an optimal threshold around 40 minutes. IL-6 and DAS28 also showed good discrimination (AUC 0.83 and 0.82, respectively). Serum iron performed in the expected inverse direction, with lower values associated with RLS and an exploratory threshold of ≤18.10. Hepcidin showed fair discrimination but lower specificity (Fig. 5).

Figure 5.

ROC curves for selected discriminators of RLS in RA patients and optimal thresholds.

Discussion

Our findings support an association between greater inflammatory burden and the presence of RLS in RA patients. Patients with RLS have higher rheumatoid arthritis activity as assessed by DAS28–CRP, higher serum IL-6 concentrations, higher hepcidin-25 concentrations, lower circulating iron, and markedly prolonged reported sleep latency. Taken together, these data point to a more specific IL-6–hepcidin–iron axis rather than a uniform cytokine signal. This pattern is compatible with an inflammation-driven disturbance of iron homeostasis. Specifically, IL-6 is a plausible upstream driver of hepcidin induction; elevated hepcidin can reduce bioavailable iron despite ferritin values that remain non-discriminatory in inflammatory states^[30] suggesting that functional iron restriction may be more informative than ferritin alone in this cohort. Ferritin becomes a blunt marker, whereas serum iron and hepcidin better capture the functional iron restriction relevant to RLS biology.

Our study focuses on one of the most common sleep impairments in RA patients, namely RLS. The exponential growth of somnology research in recent years has revealed the insidious impact of disturbed sleep on various somatic diseases, and RA is no exception.^[6] On the contrary, it is widely acknowledged that this is a disease with numerous comorbidities, but only recently have the patients’ sleep complaints, particularly RLS, received adequate attention.‌^[4-8,14] Our findings are consistent with the currently accepted hypothesis for the development of secondary RLS. They fit into a mechanistic model in which systemic inflammation and iron dysregulation provide the biochemical foundation for central sensorimotor and sleep-initiation dysfunction.^[31]

Although the “main stage” for RA is the synovial tissue and its transformation into pannus, the locally produced cytokines in high concentration gain access to the systemic circulation (spillover hypothesis). Cytokines of importance that are increased in the bloodstream are IL-1 beta, IL-6, and TNF-alpha.^[22] In the theoretical framework for RLS development, IL-6 is of interest since, through the axis of IL-6–STAT3–HAMP, it can stimulate the synthesis and release of hepcidin. The latter is recognized as the master regulator of iron metabolism in the body.^[30] Hepcidin achieves control over iron transport by inducing internalization of ferroportin channels and ultimately limiting intestinal iron absorption while blocking the available iron within the mononuclear phagocyte system. These interactions are fundamental for the development of anemia due to chronic inflammation.^[30] A meta-analysis of serum hepcidin in RA patients by Chen et al. in 2020 has shown that serum hepcidin levels are significantly higher in RA patients compared to healthy control, positively correlated with RA activity, and are associated with the presence of anemia.^[20]

Our results, indicating higher IL-6 and hepcidin and lower serum iron levels in the RLS (+) group, are in concordance with this mechanistic framework. This also implies a quantitative effect, since although serum hepcidin levels are generally elevated in RA compared to healthy controls, only a subset of RA patients develops RLS. This suggests that a higher degree of activation of the IL-6–hepcidin axis, rather than its mere presence, may be required to precipitate RLS. The lack of significant differences in ferritin levels between the two groups is consistent with functional rather than absolute iron deficiency. In this context, hepcidin-mediated alterations in iron transport may extend beyond systemic circulation, as it has been shown that hepcidin is capable of altering iron transport across blood-brain barrier affecting astrocytes and neurons^[32,33], which may precipitate RLS in RA patients^[17].

In 2012 Weinstock et al. published a theoretical paper exploring the high association of RLS with chronic inflammatory disorders, proposing that these associations are rooted in underlying mechanistic links.^[31] Almost 15 years later, accumulating evidence supports this concept, especially in patients with chronic kidney disease on dialysis.‌^[34,35] The integrative synthesis of our results supports the hypothesis for iron dysmetabolism invoked by systemic inflammation in RA. We demonstrate that higher disease activity measured by DAS28–CRP is associated with the presence of RLS and that the IL-6–hepcidin axis appears to be activated in these patients, which may also explain the observed reduction in serum iron levels.

However, a recent concise review on the role of proinflammatory cytokines in idiopathic RLS suggests that these are not recognized as reliable markers.^[36] The authors do acknowledge the methodological heterogeneity of the studies that were assessed as well as the fact that significant alterations in these cytokines exist in cohorts in which chronic inflammation is internal to the host state (e.g., patients with chronic kidney disease). The latter is also the case for our patient cohort, and it is in coherence with the proposed theoretical framework for development of secondary RLS.

Limitations

The present study has several important limitations that should be acknowledged. First, the relatively small sample size limits statistical power and increases the likelihood of type II error. Second, the monocentric and cross-sectional design precludes any inference of causality and restricts the generalizability of the findings beyond the studied population. Third, the assessment of sleep parameters relied on subjective self-reported measures without objective validation through polysomnography or actigraphy, introducing the possibility of measurement bias. In addition, residual confounding cannot be excluded, as factors such as treatment regimens, variability in disease duration, and unrecognized comorbidities may have influenced both inflammatory and iron metabolism markers. Finally, the biomarker analysis was based on single time-point measurements, which do not capture temporal dynamics and limit interpretation of the proposed IL-6–hepcidin–iron axis.

Conclusion

RA patients with RLS demonstrate higher disease activity and higher levels of hepcidin and IL-6 with lower serum iron, consistent with functional iron deficiency driven by inflammation. These findings support the role of the IL-6-hepcidin axis in the pathophysiology of secondary RLS in RA, potentially in a magnitude dependent manner.

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Additional information

Ethical statement

The authors declared that no clinical trials were used in the present study.
The authors declared that no experiments on humans or human tissues were performed for the present study.
Informed consent was obtained from the patients included in the study in accordance with the ethical standards of the Helsinki Declaration. The study was approved by the Ethics Committee of the Medical University of Plovdiv (approval No. PKHE-52/10.11.2025).

The authors declared that no experiments on animals were performed for the present study.

The authors declared that no commercially available immortalised human and animal cell lines were used in the present study.

Conflict of interest

The authors have declared that no competing interests exist.

Artificial Intelligence (AI) use

The authors accept full responsibility for the content of the manuscript, including the disclosure of any use of AI. No AI tools were used in the preparation of this manuscript.

Funding

This research was funded by the Medical University of Plovdiv (project No. P-2990/2023)

Author contributions

All authors have contributed equally.

Author ORCIDs

Todor Georgiev https://orcid.org/0000-0002-3220-6703

Kiril Terziyski https://orcid.org/0000-0003-1314-7039

Data availability

All of the data that support the findings of this study are available in the main text.