Estimating the sequence of biomarker changes in Parkinson's disease.
Parkinsonism Relat Disord 2024;
118:105939. [PMID:
38029648 DOI:
10.1016/j.parkreldis.2023.105939]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 10/30/2023] [Accepted: 11/19/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVE
To estimate the sequence of several common biomarker changes in Parkinson's disease (PD) using a novel data-driven method.
METHODS
We included 374 PD patients and 169 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI). Biomarkers, including the left putamen striatal binding ratio (SBR), right putamen SBR, left caudate SBR, right caudate SBR, cerebrospinal fluid (CSF) α-synuclein, and serum neurofilament light chain (NfL), were selected in our study. The discriminative event-based model (DEBM) was utilized to model the sequence of biomarker changes and establish the disease progression timeline. The estimated disease stages for each subject were obtained through cross-validation. The associations between the estimated disease stages and the clinical symptoms of PD were explored using Spearman's correlation.
RESULTS
The left putamen is the earliest biomarker to become abnormal among the selected biomarkers, followed by the right putamen, CSF α-synuclein, right caudate, left caudate, and serum NfL. The estimated disease stages are significantly different between PD and HC and yield a high accuracy for distinguishing PD from HC, with an area under the curve (AUC) of 0.98 (95% confidence interval 0.97-0.99), a sensitivity of 0.95, and a specificity of 0.92. Moreover, the estimated disease stages correlate with motor experiences of daily living, motor symptoms, autonomic dysfunction, and anxiety in PD patients.
CONCLUSION
We determined the sequence of several common biomarker changes in PD using DEBM, providing data-driven evidence of the disease progression of PD.
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