Back

Leveraging blood RNA expression to understand Parkinson's disease heterogeneity and progression

Gil-Martinez, A.-L.; Fairbrother-Browne, A.; Real, R.; Rocamora-Perez, G.; Brenton, J. W.; Martinez-Carrasco, A.; Botia, J. A.; Ryten, M.; Morris, H. R.

2025-12-12 neurology
10.64898/2025.12.10.25342029 medRxiv
Show abstract

Parkinsons disease (PD) exhibits substantial clinical and molecular heterogeneity, driven in part by mutations in SNCA, GBA1 and LRRK2 genes. Understanding how these genetic subtypes differ in progression and peripheral transcriptomic profiles may inform personalized prognostic and therapeutic strategies. In this study, we aim to compare disease progression trajectories and blood-based gene expression patterns among SNCA-positive (SNCA+), GBA-positive (GBA+) and LRRK2-positive (LRRK2+), and sporadic PD patients, and to identify shared and subtype-specific transcriptional signatures. We used well-characterized cohorts of SNCA+ (n=26), GBA+ (n=70), LRRK2+ (n=147), and sporadic (n=387) PD patient from the Parkinsons Progression Markers Initiative (PPMI) cohort. We used data from samples collected 6 months after baseline visit for differentially expressed gene (DEGs) analysis, controlling for cell type proportions. Our results show that SNCA+ patients had the earliest onset and poorest survival, whereas LRRK2+ and sporadic PD progressed more slowly. GBA+ and LRRK2+ patient groups exhibited prominent immune-related activation, with overlapping upregulated DEGs between LRRK2+ and GBA+ and shared downregulation between LRRK2+ and SNCA+. No significant blood DEGs distinguished favourable and unfavourable outcomes in sporadic PD cases. In conclusion, mutation-specific clinical trajectories and systemic inflammatory signatures in GBA+ and LRRK2+ PD underscore the promise of blood-based biomarkers, warranting validation in larger and tissue-targeted studies.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.