AUTHOR=Xu Xiaozhou , Zhang Shushan , Xu Chuanying , Zhang Wei , Zhao Hui , Liu Yumeng , Zhai Shilei , Zu Jie , Li Zhining , Xiao Lishun 

TITLE=Identifying subtypes of longitudinal motor symptom severity trajectories in early Parkinson’s disease patients

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1597132

DOI=10.3389/fneur.2025.1597132

ISSN=1664-2295

ABSTRACT=BackgroundMotor 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.MethodsA 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.ResultsThe 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.ConclusionSubtyping 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.