ORIGINAL RESEARCH article
Front. Neurol.
Sec. Movement Disorders
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1597132
Identifying subtypes of longitudinal motor symptom severity trajectories in early Parkinson's disease patients
Provisionally accepted- 1Xuzhou Medical University, Xuzhou, China
- 2The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- 3Second Affiliated Hospital, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
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Background: Motor symptoms of Parkinson's disease (PD) patients affect their ability of daily activities. Identifying distinct trajectories of motor symptom progression in PD patients can facilitate long-term management.Methods: A total of 155 PD patients were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI). Distinct longitudinal trajectory clusters of motor symptom progression in PD patients were identified by unsupervised self-organizing maps (SOMs), and baseline characteristics were compared among different clusters. Linear mixed-effect analysis was utilized to estimate the longitudinal courses of some cardinal motor symptoms among clusters, while survival analysis was used to compare time-to-clinical milestones within 5 years. The support vector machine (SVM) was built to predict patients' trajectory clusters, and its performance was evaluated through the mean area under the receiver-operating characteristic curve (mAUC), accuracy and macro F1-score. Shapley values were calculated to interpret individual variability.The optimal clusters by SOMs are 3. Cardinal motor symptoms of the progressive cluster worsened more rapidly, and this cluster is more likely to have impaired balance, loss of independence, sleep disturbance, and cognitive impairment within 5 years. The mAUC, accuracy, and macro F1-score of multi-class SVM model were 0.8846, 0.7692, and 0.7778, respectively. An interactive web application was developed to predict the individual's trajectory cluster.Subtyping motor symptom progression into different trajectories can improve patients' management. Using baseline data to predict which trajectory cluster a patient belongs to may help develop interventions.
Keywords: Parkinson's disease, Motor symptoms, longitudinal trajectory, unsupervised clustering, machine learning
Received: 20 Mar 2025; Accepted: 23 Jul 2025.
Copyright: © 2025 Xu, Zhao, Xu, Zhang, Liu, Zhai, Li, Zu and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhining Li, Second Affiliated Hospital, Xuzhou Medical University, Xuzhou, 221006, Jiangsu Province, China
Jie Zu, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
Lishun Xiao, Xuzhou Medical University, Xuzhou, China
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