Longitudinal plasma proteomics predict phenoconversion to clinically manifest ALS

Area of Science:

• Neuroscience
• Proteomics
• Biomarker Discovery

Background:

• Predicting phenoconversion in pre-symptomatic amyotrophic lateral sclerosis (ALS) carriers is crucial for developing preventative therapies.
• Current methods lack the ability to accurately identify individuals at risk or the timeline for disease onset.

Purpose of the Study:

• To identify protein biomarkers that predict phenoconversion in individuals with ALS-associated pathogenic variants.
• To develop a predictive model for estimating the time to clinical manifestation of ALS.

Main Methods:

• Longitudinal proteomic analysis of plasma samples from ALS patients, pre-symptomatic carriers, and controls using Olink Explore.
• Statistical modeling to identify proteins associated with phenoconversion and estimate time to disease onset.
• Replication of key findings in UK Biobank data.

Main Results:

• Identified 92 proteins with altered concentrations preceding phenoconversion.
• A core panel of 19 proteins accurately predicted phenoconversion within 0.5 to 5 years (AUC 0.80-0.89).
• The panel estimated time to phenoconversion with a mean absolute error of 1.6 years, outperforming single markers like NEFL.

Conclusions:

• A multi-protein panel serves as a robust predictor of pre-symptomatic ALS phenoconversion.
• These biomarkers offer insights into ALS pathogenesis and advance the goal of disease prevention.
• The findings support the development of novel diagnostic and prognostic tools for ALS.