Abstract
Abstract
Parkinson’s disease (PD) is a heterogeneous neurodegenerative disorder with variable long-term outcomes. Blood-based biomarkers for prognostic prediction remain underdeveloped. We aimed to develop a peripheral blood transcriptomic signature for predicting long-term complications in PD using unsupervised data-driven methods. Using RNA sequencing data from 541 PD patients and 180 healthy controls from the Parkinson’s Progression Markers Initiative (PPMI), we employed weighted gene correlation network analysis (WGCNA) to identify disease-associated gene modules. Contrastive principal component analysis was applied to derive a pseudo-temporal (PT) trajectory score reflecting molecular disease progression. The prognostic value of PT scores was assessed through Cox regression for cognitive impairment, freezing of gait (FOG), wearing-off, and levodopa-induced dyskinesia. WGCNA identified two PD-associated co-expression modules enriched for immune/inflammatory pathways. PT scores derived from these modules showed significant correlations with cognitive, autonomic, and axial motor symptoms. In multivariable Cox regression, higher PT scores independently predicted cognitive impairment (HR = 7.31,
P
= 0.002) and FOG (HR = 3.43,
P
< 0.001), but not predominantly dopaminergic motor complications. These findings demonstrate that peripheral blood transcriptomic signatures capture aspects of PD pathophysiology that are not fully explained by nigrostriatal dopaminergic degeneration alone, serving as potential prognostic biomarkers for cognitive impairment and FOG.