Abstract
Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several known risk factors, ICD development cannot yet be accurately predicted at PD diagnosis. We aimed to investigate the predictability of incident ICD by baseline measures of demographic, clinical, dopamine transporter single photon emission computed tomography and single nucleotide polymorphisms data of medication-free PD patients, obtained from the Parkinson’s Progression Markers Initiative (PPMI; n = 311) and Amsterdam University Medical Center (UMC; n = 72) longitudinal datasets. We trained machine learning models to predict incident ICD at any follow-up assessment. The highest predictive performance (AUC = 0.66) was achieved by clinical features only. We observed significantly higher performance (AUC = 0.74) when classifying patients who developed ICD within four years from diagnosis compared with those tested negative for seven or more years. Overall, prediction accuracy for later ICD development at the time of PD diagnosis is limited, but increases for shorter time-to-event predictions.
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Data availability
Data used in the preparation of this article was obtained on 1-9-2023 from the PPMI database (http://www.ppmi-info.org/access-dataspecimens/download-data) RRID:SCR_006431. For up-to-date information on the study, visit www.ppmi-info.org. The Amsterdam UMC dataset is not publicly available according to GDPR.
Code availability
Codes are available at www.github.com/sciqd/Learn_2_control.
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Acknowledgements
This research was funded in whole (Grant number MJFF-022801) by the Michael J. Fox Foundation for Parkinson's Research (MJFF). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research, and funding partners; 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.
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A.V.: Data curation, Investigation, Methodology, Image processing, Image analysis, Machine Learning analysis, Writing original draft. T.vB.: Data curation, Investigation, Univariate Analysis, Writing original draft, Writing review & editing. G.vW., J.B., D.W., H.B., O.vdH.: Validation, Writing review & editing, C.V.: Conceptualization, Funding acquisition, Investigation, Methodology, Validation, Writing review & editing, Project administration, Supervision.
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J.B. is a consultant at GE Healthcare (all related payments to the institute). D.W. receives research funding and salary support from the Michael J. Fox Foundation for serving on the Executive Steering Committee of the Parkinson’s Progression Markers Initiative study. The authors declare no other financial or non-financial competing interests.
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Vamvakas, A., Van Balkom, T., Van Wingen, G. et al. Prediction of impulse control disorders in Parkinson’s disease through a longitudinal machine learning study. npj Parkinsons Dis. (2026). https://doi.org/10.1038/s41531-025-01248-w
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DOI: https://doi.org/10.1038/s41531-025-01248-w
