Impulse control disorders and related behaviors in Parkinson's disease: risk factors, clinical and genetic aspects, and management. Issue 4 (August 2021)

• Record Type: Journal Article
• Authors: Faouzi, Johann; Corvol, Jean-Christophe; Mariani, Louise-Laure
• Is Part Of: Current opinion in neurology. Volume 34:Issue 4(2021)
• Publication Date: 2021-08
• Subjects: dopamine agonists -- dopamine replacement therapy -- impulse control disorders -- Parkinson's disease -- precision medicine; Neurology -- Periodicals; Nervous system -- Diseases -- Periodicals; 616.805
• DOI: 10.1097/WCO.0000000000000955
• Languages: English
• ISSNs: 1473-6551

Abstract

Abstract : Purpose of review: To review recent findings and research directions on impulse control disorders and related behaviors (ICDRBs) in Parkinson's disease (PD). Recent findings: Longitudinal studies found that prevalence increases during PD progression, incident ICDRBs being around 10% per year in patients treated with dopaminergic therapies. Screening tools and severity scales already developed have been validated and are available in several countries and languages. The main clinical risk factors include young age, male gender, type, doses and duration of dopaminergic therapy, PD motor severity and dyskinesia, depression, anxiety, apathy, sleep disorders, and impulsivity traits. Genetic factors are suspected by a high estimated heritability, but individual genes and variants remain to be replicated. Management of ICDRBs is centered on dopamine agonist decrease, with the risk to develop withdrawal symptoms. Cognitive behavioral therapy and subthalamic nucleus deep brain stimulation also improve ICDRBs. In the perspective of precision medicine, new individual prediction models of these disorders have been proposed, but they need further independent replication. Summary: Regular monitoring of ICDRB during the course of PD is needed, particularly in the subject at high risk of developing these complications. Precision medicine will require the appropriate use of machine learning to be reached in the clinical setting.