Exploratory analysis of the genetics of impulse control disorders in Parkinson's disease using genetic risk scores

Impulse control disorder: Review on clinical, pharmacologic, and genetic risk factors

INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, among which impulse control disorders behaviors (ICD) emerge as significant non-motor manifestations. ICD in PD patients, including pathological gambling, hypersexuality, compulsive buying, among others, lead to considerable impairment and reduced quality of life. This review aims to explore the multifaceted risk factors associated with ICD in PD patients, including clinical, pharmacological, and genetic aspects, to enhance early identification, prevention, and management strategies.

METHODS

A comprehensive review of literature was conducted to identify studies investigating risk factors for ICD in PD. Data from clinical, pharmacological, and genetic studies were analyzed to elucidate the complex interplay of factors contributing to ICD development.

RESULTS

Clinical risk factors such as young age, male gender, and specific personality traits were consistently associated with a higher incidence of ICD. Environmental factors such as cultural nuances and geographic location influence ICD prevalence. Disease characteristics include early PD onset, longer disease duration, motor fluctuations, anxiety, depression, sleep disorders, and apathy. Pharmaceutical risk factors involve dopaminergic drugs, with dopamine agonists showing a dose-dependent association with ICD. Genetic risk factors highlight the involvement of dopaminergic and serotoninergic systems, with various neurotransmitter pathways implicated.

CONCLUSIONS

ICDs are common and severe in PD. Understanding the multifaceted risk factors for ICD in PD is crucial for identifying patients at high risk to develop these adverse effects and developing targeted interventions to prevent their occurrence. Given their frequency and potential consequences for the patient and their family, the current strategy is to systematically screen for ICDs throughout patient follow-up, particularly when prescribing dopamine agonists.

DRD4 gene polymorphism and impulse control disorder induced by dopamine agonists in Parkinson's disease

Impulse control disorders and their consequences display variability among individuals, indicating potential involvement of environmental and genetic factors. In this retrospective study, we analyzed a cohort of Parkinson's disease patients treated with dopamine agonists and investigated the influence of the dopamine D4 receptor gene polymorphism, DRD4 7R+, which is linked to psychiatric disorders, impulsive traits, and addictive behaviors. We found that DRD4 7R+ is a significant genetic risk factor associated with the severity of ICD.

Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review

Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.

Performance on the balloon analogue risk task and anticipatory response inhibition task is associated with severity of impulse control behaviours in people with Parkinson's disease

Dopamine agonist medication is one of the largest risk factors for development of problematic impulse control behaviours (ICBs) in people with Parkinson's disease. The present study investigated the potential of dopamine gene profiling and individual performance on impulse control tasks to explain ICB severity. Clinical, genetic and task performance data were entered into a mixed-effects linear regression model for people with Parkinson's disease taking (n = 50) or not taking (n = 25) dopamine agonist medication. Severity of ICBs was captured via the Questionnaire for Impulsive-compulsive disorders in Parkinson's disease Rating Scale. A cumulative dopamine genetic risk score (DGRS) was calculated for each participant from variance in five dopamine-regulating genes. Objective measures of impulsive action and impulsive choice were measured on the Anticipatory Response Inhibition Task and Balloon Analogue Risk Task, respectively. For participants on dopamine agonist medication, task performance reflecting greater impulsive choice (p = 0.014), and to a trend level greater impulsive action (p = 0.056), as well as a longer history of DA medication (p < 0.001) all predicted increased ICB severity. DGRS however, did not predict ICB severity (p = 0.708). No variables could explain ICB severity in the non-agonist group. Our task-derived measures of impulse control have the potential to predict ICB severity in people with Parkinson's and warrant further investigation to determine whether they can be used to monitor ICB changes over time. The DGRS appears better suited to predicting the incidence, rather than severity, of ICBs on agonist medication.

Machine learning-based prediction of impulse control disorders in Parkinson's disease from clinical and genetic data

Goal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson’s disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort. Methods: We used data from two longitudinal PD cohorts (training set: PPMI, Parkinson’s Progression Markers Initiative; test set: DIGPD, Drug Interaction With Genes in Parkinson’s Disease). We included 380 PD subjects from PPMI and 388 PD subjects from DIGPD, with at least two visits and with clinical and genetic data available, in our analyses. We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs. We quantified performance using the area under the receiver operating characteristic curve (ROC AUC) and average precision. We compared these models to a trivial model predicting ICDs at the next visit with the status at the most recent visit. Results: The recurrent neural network (PPMI: 0.85 [0.80 – 0.90], DIGPD: 0.802 [0.78 – 0.83]) was the only model to be significantly better than the trivial model (PPMI: ROC AUC = 0.75 [0.69 – 0.81]; DIGPD: 0.78 [0.75 – 0.80]) on both cohorts. We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model. The improvement in terms of ROC AUC was higher on PPMI than on DIGPD data, but not clinically relevant in both cohorts. Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance.

Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson's Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons. The vast majority of PD patients develop the disease sporadically and it is assumed that the cause lies in polygenic and environmental components. The overall polygenic risk is the result of a large number of common low-risk variants discovered by large genome-wide association studies (GWAS). Polygenic risk scores (PRS), generated by compiling genome-wide significant variants, are a useful prognostic tool that quantifies the cumulative effect of genetic risk in a patient and in this way helps to identify high-risk patients. Although there are limitations to the construction and application of PRS, such as considerations of limited genetic underpinning of diseases explained by SNPs and generalizability of PRS to other populations, this personalized risk prediction could make a promising contribution to stratified medicine and tailored therapeutic interventions in the future.