Parkinson's Disease motor and non-motor progression models emerge from pathway-level transcriptomics
Journal:
medRxiv
Published Date:
Feb 27, 2026
Abstract
Background Prognosis and therapeutic management in Parkinson's disease is a challenging task by its highly heterogeneous disease progression and symptoms presentation, lacking biomarkers to predict individual disease trajectories. Objective To determine whether baseline blood transcriptomes, analyzed through biologically defined pathway gene sets, contain signatures that distinguish distinct motor and non-motor progression trajectories in Parkinson's disease. Methods We developed a pathway-based computational framework to derive individualized molecular severity scores from baseline blood transcriptomic profiles, integrating pathway-level gene expression with longitudinal clinical data. Domain-specific severity indices were generated for motor and non-motor features and used to model progression trajectories in sporadic Parkinson's disease within the Parkinson's Progression Markers Initiative cohort. Machine learning models assessed baseline predictability of trajectory membership. Findings were independently validated in genetic cohorts and externally in the Parkinson's Disease Biomarkers Program cohort. Results Baseline molecular severity scores were associated with key clinical features. Analysis of score changes revealed two distinct non-motor and two distinct motor progression groups, each underpinned by specific gene signatures (20 genes for non-motor; 121 for motor). Machine learning models predicted an individual's trajectory group from baseline transcriptomic data with high accuracy (0.87 for motor progression). The framework demonstrated robust generalizability across independent and genetic cohorts, producing clinically coherent profiles. Conclusions Blood transcriptomes define clinically distinct PD progression subtypes via pathway-level signatures, with motor and non-motor trajectories driven by largely independent molecular programs from baseline. This biologically informed stratification is generalizable across cohorts and provides a novel framework for estimating patient prognosis and moving toward precision medicine in Parkinson's disease.
