Heterogeneity in the severity of Parkinson's disease (PD) inhibits the effective interpretation of clinical trial outcomes. Multi-omics analysis may help explain the pathological mechanisms underlying disease progression and reveal biomarkers of clinical severity. We performed Multi-Omics Factor Analysis (MOFA) on whole blood RNA, miRNA and cerebrospinal fluid (CSF) and blood plasma proteomics from the Parkinson's Progression Marker Initiative (PPMI), to identify molecular factors correlated with motor (MDS-UPDRS3) and cognitive (Semantic Fluency Test, SFT) function. Three molecular factors significantly correlated with the MDS-UPDRS3 score and two with SFT, which remained significant after adjusting for age, sex, and medication dose. We used the identified factors to stratify patients into subgroups with distinct motor and cognitive severity. The severe motor clusters showed deregulation of cytotoxic natural killer cell mechanisms in peripheral blood, and changes to proteins associated with the endoplasmic reticulum and dense core vesicle in CSF. The severe cognitive clusters showed changes in the complement system and synaptic dysfunction. Our analysis capitalizes on multi-omics data integration to enrich our understanding of the mechanisms driving motor and cognitive decline in PD, to support precision medicine.
Athieniti et al. (2026) studied this question.