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Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson's Disease From Clinical and Genetic Data.

ABSTRACT: 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.

SUBMITTER: Faouzi J

PROVIDER: S-EPMC9252337 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCurrent evidence on management of impulse control disorders (ICDs) in Parkinson's disease (PD) remains scarce, and exploring modifiable risk factors is crucial.ObjectiveWe evaluated the profiles of ICDs in PD patients and aimed to determine the associations between ICDs, metabolic syndrome components and other clinical features.MethodsWe enrolled patients diagnosed with PD in this study and conducted comprehensive clinical assessments.ResultsWe recruited 39 PD patients with ICDs and 66 PD patients without ICDs. Out of the 39 patients with ICDs, 19 (48.7%) had one impulse control disorder, while 20 (51.3%) had two or more. The most commonly reported symptom of ICDs was compulsive eating (48.7%). Significant differences were observed between the PD patients with and without ICDs in terms of their HbA1c levels, history of diabetes mellitus, dopamine agonist use, levodopa equivalent dose of dopamine agonists (LED DA), and Hamilton Depression Rating Scale (HAMD) scores. HbA1c levels were significantly higher in the PD patients with compulsive eating. Stepwise logistic regression analyses were performed with the dependent variables of ICDs (yes/no) and compulsive eating (yes/no). Among the 105 PD patients, those with ICDs exhibited higher levels of HbA1c, HAMD score and LED DA than those without ICDs (p < 0.01). Among 39 PD patients with ICDs, those with compulsive eating exhibited higher levels of HbA1c (OR = 2.148, 95% CI = 1.004-4.594, p < 0.05). Among 105 PD patients, those with compulsive eating exhibited higher levels of HbA1c, LED DA and HAMD score (p < 0.05).ConclusionThis study provides insights into the profiles of ICDs in PD patients and their associations with various clinical features. Compulsive eating was the most common ICDs symptom reported. Notably, HbA1c levels were found to be higher in patients with compulsive eating, indicating that poor blood glucose control may be a potential risk factor for ICDs in PD. However, it should be noted that the higher HbA1c levels could also be a consequence of compulsive eating rather than a causal factor for ICDs in PD. Further research is needed to confirm the modifiable risk factors for ICDs in PD.

Project description:IntroductionImpulse 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.MethodsWhole-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.ResultsAmong 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.ConclusionUsing 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.

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Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson's Disease From Clinical and Genetic Data.

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