AUTHOR=Erga Aleksander H. , Dalen Ingvild , Ushakova Anastasia , Chung Janete , Tzoulis Charalampos , Tysnes Ole Bjørn , Alves Guido , Pedersen Kenn Freddy , Maple-Grødem Jodi TITLE=Dopaminergic and Opioid Pathways Associated with Impulse Control Disorders in Parkinson’s Disease JOURNAL=Frontiers in Neurology VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2018.00109 DOI=10.3389/fneur.2018.00109 ISSN=1664-2295 ABSTRACT=Introduction

Impulse control disorders (ICDs) are frequent non-motor symptoms in Parkinson’s disease (PD), with potential negative effects on the quality of life and social functioning. ICDs are closely associated with dopaminergic therapy, and genetic polymorphisms in several neurotransmitter pathways may increase the risk of addictive behaviors in PD. However, clinical differentiation between patients at risk and patients without risk of ICDs is still troublesome. The aim of this study was to investigate if genetic polymorphisms across several neurotransmitter pathways were associated with ICD status in patients with PD.

Methods

Whole-exome sequencing data were available for 119 eligible PD patients from the Norwegian ParkWest study. All participants underwent comprehensive neurological, neuropsychiatric, and neuropsychological assessments. ICDs were assessed using the self-report short form version of the Questionnaire for Impulsive-Compulsive Disorders in PD. Single-nucleotide polymorphisms (SNPs) from 17 genes were subjected to regression with elastic net penalization to identify candidate variants associated with ICDs. The area under the curve of receiver-operating characteristic curves was used to evaluate the level of ICD prediction.

Results

Among the 119 patients with PD included in the analysis, 29% met the criteria for ICD and 63% were using dopamine agonists (DAs). Eleven SNPs were associated with ICDs, and the four SNPs with the most robust performance significantly increased ICD predictability (AUC = 0.81, 95% CI 0.73–0.90) compared to clinical data alone (DA use and age; AUC = 0.65, 95% CI 0.59–0.78). The strongest predictive factors were rs5326 in DRD1, which was associated with increased odds of ICDs, and rs702764 in OPRK1, which was associated with decreased odds of ICDs.

Conclusion

Using an advanced statistical approach, we identified SNPs in nine genes, including a novel polymorphism in DRD1, with potential application for the identification of PD patients at risk for ICDs.