Integrating genetic and clinical data to predict impulse control disorders in Parkinson's disease

Medical, surgical, and physical treatments for Parkinson's disease

Although dopamine replacement therapy remains a core component of Parkinson's disease treatment, the onset of motor fluctuations and dyskinetic movements might require a range of medical and surgical approaches from a multidisciplinary team, and important new approaches in the delivery of dopamine replacement are becoming available. The more challenging, wide range of non-motor symptoms can also have a major impact on the quality of life of a patient with Parkinson's disease, and requires careful multidisciplinary management using evidence-based knowledge, as well as appropriately tailored strategies according to the individual patient's needs. Disease-modifying therapies are urgently needed to prevent the development of the most disabling refractory symptoms, including gait and balance difficulties, cognitive impairment and dementia, and speech and swallowing impairments. In the third paper in this Series, we present the latest evidence supporting the optimal treatment of Parkinson's disease, and describe an expert approach to many aspects of treatment choice where an evidence base is insufficient.

Anxiety predicts impulsive-compulsive behaviours in Parkinson's disease: Clinical relevance and theoretical implications

Patients with Parkinson's disease (PD) often present symptoms of anxiety, depression and apathy. These negative affect manifestations have been recently associated with the presence of impulsive compulsive behaviours (ICBs). However, their relation with the use of dopamine replacement therapy (DRT), a renewed risk factor for ICBs, is still not fully understood. Elucidating the role of these different ICBs predictors in PD could inform both prevention/intervention recommendations as well as theoretical models. In the present study, we have analysed data collected in 417 PD patients, 50 patients with Parkinsonian symptoms but with scan without evidence of dopaminergic deficit (SWEDD), and 185 healthy controls (HC). We examined each patient's clinical profile over a two-year time window, investigating the role of both negative affect and DRT on ICBs. Results confirmed the presence of higher levels of anxiety in both the clinical groups, and of higher level of ICBs in SWEDD patients, respect to both PD and HC. Mixed model analyses revealed a statistically significant association between anxiety and ICBs in the SWEDD patients who did not take any DRT. Findings suggest the independence between anxiety and DRT in ICBs development, and provide new evidence for the motivational opponency theoretical framework.

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.

Assessment of therapeutic strategies for management of impulse control disorder in Parkinson's disease

BACKGROUND

Impulse control disorders (ICD) occur frequently in individuals with Parkinson's disease. So far, prevention is the best treatment. Several strategies for its treatment have been suggested, but their frequency of use and benefit have scarcely been explored.

OBJECTIVE

To investigate which strategy is the most commonly used in a real-life setting and its rate of response.

METHODS

A longitudinal study was conducted. At the baseline evaluation, data on current treatment and ICD status according to QUIP-RS were collected. The treatment strategies were categorized as "no-change", dopamine agonist (DA) dose lowering, DA removal, DA switch or add-on therapy. At the six-month follow-up visit, the same tools were applied.

RESULTS

A total of 132 individuals (58.3% men) were included; 18.2% had at least one ICD at baseline. The therapeutic strategy most used in the ICD group was no-change (37.5%), followed by DA removal (16.7%), DA switch (12.5%) and DA lowering (8.3%). Unexpectedly, in 20.8% of the ICD subjects the DA dose was increased. Overall, nearly 80% of the subjects showed remission of their ICD at follow-up.

CONCLUSIONS

Regardless of the therapy used, most of the subjects presented remission of their ICD at follow-up Further research with a longer follow-up in a larger sample, with assessment of decision-making processes, is required in order to better understand the efficacy of strategies for ICD treatment.

Impulse control disorders and related behaviors in Parkinson's disease: risk factors, clinical and genetic aspects, and management

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.

Impulse Control Disorders in Parkinson's Disease: From Bench to Bedside

Parkinson’s disease (PD) is a neurodegenerative disorder that is characterized by symptoms that impact both motor and non-motor domains. Outside of motor impairments, PD patients are at risk for impulse control disorders (ICDs), which include excessively disabling impulsive and compulsive behaviors. ICD symptoms in PD (PD + ICD) can be broadly conceptualized as a synergistic interaction between dopamine agonist therapy and the many molecular and circuit-level changes intrinsic to PD. Aside from discontinuing dopamine agonist treatment, there remains a lack of consensus on how to best address ICD symptoms in PD. In this review, we explore recent advances in the molecular and neuroanatomical mechanisms underlying ICD symptoms in PD by summarizing a rapidly accumulating body of clinical and preclinical studies, with a special focus on the utility of rodent models in gaining new insights into the neurochemical basis of PD + ICD. We also discuss the relevance of these findings to the broader problem of impulsive and compulsive behaviors that impact a range of neuropsychiatric syndromes.