Elucidating Molecular Networks Underpinning Heterogeneity in Parkinson’s Disease Progression Across Clinical Manifestation Spectrum

Abstract

AbstractIn this study, we applied statistical and machine learning techniques to identify molecular mechanisms underlying the heterogeneity in individual Parkinson’s Disease (PD) progression. Leveraging data from the Parkinson’s Progression Markers Initiative (PPMI) cohort, we analyzed genetic and clinical data for patients with PD, focusing on traits including motor symptoms, non-motor symptoms, and biomarkers. Our method identified significant single-nucleotide polymorphisms (SNPs) associated with each PD trait, revealing key genetic factors and their impact on disease progression. Furthermore, through network medicine approaches, we delineated disease modules, uncovering unique gene clusters and their roles in PD pathology. The integration of pathway enrichment analysis further enhanced our understanding of the functional implications of these genetic variations, notably highlighting the significance of cellular stress response and protein aggregation pathways in PD. Overall, our findings offer a comprehensive view of the genetic landscape of PD progression, highlighting the potential of personalized medicine in managing this complex disease.