Blood Transcriptomics Predicts Parkinson's Motor Progression

A study in Frontiers in Digital Health applies an explainable ensemble of Random Forest, XGBoost, and SVM models to baseline whole-blood RNA‑seq from PPMI to predict Parkinson’s motor progression. Using SHAP interpretability, it highlights immune, mitochondrial, and synaptic gene pathways tied to MDS‑UPDRS Part III trajectories. Authors argue this peripheral biomarker approach could noninvasively stratify patients and improve trial efficiency, though external clinical validation remains necessary.