Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers

Frontiers in Aging Neuroscience

Ling-Yan Ma¹  Yu Tian³  Chang-Rong Pan³  Jing-Song Li³  Zhong-Lue Chen⁴  Kang Ren⁴  Yun Ling⁴  Tao Feng⁵ 

[1] China National Clinical Research Center for Neurological Diseases, Beijing, China;Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;Engineering Research Center of Electronic Medical Record (EMR) and Intelligent Expert System, College of Biomedical Engineering and Instrument Science, Zhejiang University, Ministry of Education, Hangzhou, China;Gyenno Science Co. Ltd., Shenzhen, China;Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China;

关键词: Parksinon's disease;    motor progression;    predictive model;    Parkinson's progression markers initiative;    machine learning;   

DOI  :  10.3389/fnagi.2020.627199

来源: DOAJ

【 摘 要 】

Background: The substantial heterogeneity of clinical symptoms and lack of reliable progression markers in Parkinson's disease (PD) present a major challenge in predicting accurate progression and prognoses. Increasing evidence indicates that each component of the neurovascular unit (NVU) and blood-brain barrier (BBB) disruption may take part in many neurodegenerative diseases. Since some portions of CSF are eliminated along the neurovascular unit and across the BBB, disturbing the pathways may result in changes of these substances.Methods: Four hundred seventy-four participants from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023) were included in the study. Thirty-six initial features, including general information, brief clinical characteristics and the current year's classical scale scores, were used to build five regression models to predict PD motor progression represented by the coming year's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III score after redundancy removal and recursive feature elimination (RFE)-based feature selection. Then, a threshold range was added to the predicted value for more convenient model application. Finally, we evaluated the CSF and blood biomarkers' influence on the disease progression model.Results: Eight hundred forty-nine cases were included in the study. The adjusted R2 values of three different categories of regression model, linear, Bayesian and ensemble, all reached 0.75. Models of the same category shared similar feature combinations. The common features selected among the categories were the MDS-UPDRS Part III score, Montreal Cognitive Assessment (MOCA) and Rapid Eye Movement Sleep Behavior Disorder Questionnaire (RBDSQ) score. It can be seen more intuitively that the model can achieve certain prediction effect through threshold range. Biomarkers had no significant impact on the progression model within the data in the study.Conclusions: By using machine learning and routinely gathered assessments from the current year, we developed multiple dynamic models to predict the following year's motor progression in the early stage of PD. These methods will allow clinicians to tailor medical management to the individual and identify at-risk patients for future clinical trials examining disease-modifying therapies.

【 授权许可】

Unknown