Department of Neurology, Zhengzhou University People's Hospital (Henan Provincial People's Hospital), Zhengzhou, 450003, Henan, China.
Department of Neurology, Henan University People's Hospital, Zhengzhou, 450003, Henan, China.
J Neurol. 2024 Oct;271(10):6791-6800. doi: 10.1007/s00415-024-12645-1. Epub 2024 Aug 26.
Parkinson's disease (PD) demonstrates considerable heterogeneity in the manifestation of clinical symptoms and disease progression. Recently, six clinical milestones have been proposed to evaluate disease severity in PD. However, the identification of PD progression subtypes based on these milestone events has not yet been performed.
Latent class analysis (LCA) was employed to identify subtypes of PD progression based on the timing of the first occurrence of six milestones within a 6-year follow-up period in Parkinson's Progression Markers Initiative (PPMI) database.
The study cohort consisted of 354 early PD patients, of whom 42.9% experienced at least one milestone within six years. LCA identified two distinct subtypes of PD progression: slow progression (83%) and rapid progression (17%). The total number of milestones over six years was significantly higher in the rapid progression subtype compared to the slow progression subtype (median: 3.00 vs. 0.00, p < 0.001). At baseline, the rapid progression subtype, compared to the slow progression subtype, was characterized by an older age at onset and more severe motor and non-motor symptoms. On biomarkers, the rapid progression subtype demonstrated elevated CSF p-tau and serum NFL, but decreased mean striatal DAT uptake. Five clinical variables (age, SDMT score, MDS-UPDRS I score, MDS-UPDRS II + III scores, and RBD) were selected to construct the predictive model. The original predictive model achieved an AUC of 0.82. In internal validation using bootstrap resampling, the model achieved an AUC of 0.82, with a 95%CI ranging from 0.76 to 0.87. The model's performance was acceptable regarding both calibration and clinical utility.
Approximately 17% of early PD patients exhibited the rapid progression subtype, characterized by the occurrence of more and earlier-onset milestones. The nomogram predictive model, incorporating five baseline clinical variables (age, SDMT score, MDS-UPDRS I score, MDS-UPDRS II + III scores, RBD), serves as a valuable tool for prognostic counseling and patient selection in PD clinical trials.
帕金森病（PD）在临床症状和疾病进展方面表现出相当大的异质性。最近，提出了六个临床里程碑来评估 PD 的疾病严重程度。然而，基于这些里程碑事件，尚未确定 PD 进展的亚型。
采用潜在类别分析（LCA）根据帕金森进展标志物倡议（PPMI）数据库中 6 年随访期间首次出现六个里程碑的时间，确定 PD 进展亚型。
研究队列包括 354 名早期 PD 患者，其中 42.9%在 6 年内至少经历过一个里程碑。LCA 确定了两种不同的 PD 进展亚型：缓慢进展（83%）和快速进展（17%）。在快速进展亚型中，六年内的里程碑总数明显高于缓慢进展亚型（中位数：3.00 与 0.00，p<0.001）。在基线时，与缓慢进展亚型相比，快速进展亚型的发病年龄更大，且运动和非运动症状更严重。在生物标志物方面，快速进展亚型表现出较高的 CSF p-tau 和血清 NFL，但纹状体 DAT 摄取量减少。选择五个临床变量（年龄、SDMT 评分、MDS-UPDRS I 评分、MDS-UPDRS II+III 评分和 RBD）构建预测模型。原始预测模型的 AUC 为 0.82。使用 bootstrap 重采样进行内部验证时，该模型的 AUC 为 0.82，95%CI 范围为 0.76 至 0.87。该模型在校准和临床实用性方面的性能均令人满意。
约 17%的早期 PD 患者表现出快速进展亚型，其特征是出现更多和更早出现的里程碑。纳入五个基线临床变量（年龄、SDMT 评分、MDS-UPDRS I 评分、MDS-UPDRS II+III 评分、RBD）的列线图预测模型可作为 PD 临床试验中预后咨询和患者选择的有用工具。
