A Plasma Proteomic Biomarker Panel for Early Detection & Disease Prediction in ALS
- Utilizing high throughput plasma proteomics to define a 33 protein biomarker panel that differentiates ALS from controls and other neurological diseases with high classification accuracy
- Applying machine learning to integrate plasma protein levels with clinical features, achieving robust diagnostic performance (AUC ~98%) and a composite ALS risk score
- Demonstrating that shifts in the proteomic risk score emerge years (up to ~10 yrs) before clinical symptom onset, highlighting a prodromal phase amenable to early detection and intervention
- Discussing how multi protein signatures and pathway insights (skeletal muscle, neuronal and energy metabolism processes) can inform prognostic stratification and future therapeutic targeting, including broader validation and global applicability