Parkinson’s Disease motor and non-motor progression models emerge from pathway-level transcriptomics

Abstract Background Prognosis and therapeutic management in Parkinson’s disease remain challenging due to the disease’s heterogeneous progression and symptom presentation and lack of reliable 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 Using data from the Parkinson’s Progression Markers Initiative cohort, we developed a pathway-based computational framework to derive individualized molecular severity scores from baseline blood transcriptomic profiles by integrating pathway-level gene expression with longitudinal clinical data. Severity indices for motor and non-motor features established domain-specific progression trajectories of sporadic Parkinson’s disease. Machine learning models were trained to predict patient trajectory membership from baseline transcriptomics. Findings were validated in genetic subcohorts and externally in the Parkinson’s Disease Biomarkers Program cohort. Results Molecular severity scores were associated with key clinical features. Analysis of score changes revealed two non-motor and two motor progression groups, each characterized by specific gene signatures (20 genes for non-motor; 121 for motor). From baseline transcriptomic data, we accurately predicted an individual’s trajectory group (0.87 for motor progression). The framework demonstrated high generalizability across independent and genetic cohorts, producing clinically coherent profiles. Conclusions Our analysis reveals that baseline blood transcriptomic profiles delineate motor and non-motor progression trajectories in sporadic Parkinson’s disease. The results are consistent with prior findings and may contribute to the identification of novel biomarkers, thereby informing and potentially optimizing the design of clinical trials aimed at modifying disease progression.