Applying big data to improve mental health outcomes with restless legs syndrome treatment

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PROJECT SUMMARY/ABSTRACT Restless legs syndrome (RLS) is a complex neurological and sleep disorder that has a high comorbid prevalence of mental health conditions. The cardinal feature of RLS is symptom manifestation at nighttime that disrupts restful sleep and subsequent daytime functioning. Risks for new onset and exacerbations of mental health conditions are further increased because of psychiatric adverse effects (AEs) associated with RLS pharmacotherapy. Dopamine agonists (DAs) and gabapentinoids are first-line treatments for moderate-to- severe primary RLS. DAs are associated with serious AEs including hallucinations, psychosis, symptoms of mania and most notably, impulse control disorders (ICDs). The most common ICD is gambling disorder, but there are other related impulsive behaviors that are less described. These effects have been well described in Parkinson’s disease treated with DAs, but the magnitude of risk in RLS is not well known. Given these risks and other safety concerns, preference for RLS treatment has shifted to gabapentinoids; however, gabapentinoids carry risks of suicidality. Although this effect is rare, it is not clear if the safety profile of gabapentinoids is ideal for long-term use in RLS patients who are at risk for anxiety, depression, insomnia, and suicidality independent of RLS treatment use. Therefore, the objective of this R36 Dissertation Award is to conduct a population-based assessment of mental health outcomes among treated RLS patients using pharmacoepidemiological methods. This proposal will employ big data, or real-world data (RWD), consisting of nationally representative health care encounters and self-reported health data. In Aim 1, drug utilization research methods will be used to characterize RLS pharmacotherapy utilization and prescribing among the primary early-onset RLS population including pre-existing mental health comorbidities using RWD from 2012- 2019. In Aim 2, DAs and gabapentinoids will be compared to assess the relationship between each treatment class on the risk of onset or worsening of mental health conditions. Emergency department and inpatient data will be used to detect exacerbations of mental health comorbidities. In Aim 3, DAs and gabapentinoids will be compared on the risk of onset of ICDs and suicidality. An innovative exploratory outcome of impulse control behaviors will be developed. Overall, this research aims to improve the mental health burden of the RLS population, to add to the literature on ICDs, to contribute to suicide prevention research, and to demonstrate application of pharmacoepidemiology within mental health research. This project serves as the trainee’s doctoral dissertation and provides an invaluable opportunity to facilitate transition to an independent research scientist. Through the proposed project and supervision under a robust mentorship team, the trainee will achieve her long-term goal of pursuing a research career applying her passion for mental health, drug safety, and public health.