Prevalence of restless legs syndrome in people with diabetes mellitus: A pooling analysis of observational studies

Abstract

SummaryBackgroundDiabetes mellitus (DM) is associated with different clinical complications. The aim of this study was to explore the prevalence of RLS in people with diabetes mellitus and compare the risk of restless leg syndrome (RLS) between diabetic and non-diabetic population.MethodsWe searched for studies of RLS prevalence in DM through PubMed, Embase, and Web of Science. Two authors independently completed the literature screening, data extraction, and bias risk assessment of eligible studies. All observational studies that assessed the prevalence or risk of RLS in DM were included, where the diagnosis of RLS was based on the International Restless Legs Syndrome Study Group (IRLSSG). Percentages, odds ratio (OR) with 95% confidence intervals (CI) were used to assess pooled estimates of RLS prevalence and risk based on random-effects models. Newcastle-Ottawa-scale (NOS) or a modified NOS were used to evaluate the quality of studies.FindingsA total of 42 studies, including 835,986 participants, met the eligibility criteria for the meta-analysis. Among them, 30 studies were included in meta-analysis to analyze the prevalence of RLS. A second meta-analysis was conducted using 31 studies to determine RLS risk between diabetes and non-diabetes. The results indicate that between 25% (95% confidence interval 21%-29%) of people with diabetes showed signs of RLS, and people with diabetes had an increased risk of developing RLS compare to people without diabetes (OR 1.98, 95%CI 1.66- 2.34, p < 0.001). However, the available evidence was limited due to potential risk of bias and variability between studies (I2>75%), all of observational design.InterpretationOur study suggests that the prevalence and risk of RLS might be higher in DM patients than in non-diabetes population. However, given limitations in the analysis and study design, the findings need to be corroborated in future studies.FundingThis work was supported by the Basic Conditions Platform Construction Project of Sichuan Science and Technology Department (2019JDPT0015), and the “1・3・5 project for disciplines of excellence, West China Hospital, Sichuan University” (ZYJC18003).