| Title: | Longitudinal plasma proteomics predict phenoconversion to clinically manifest ALS |
| Journal: | Nature Medicine |
| Published: | 27 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42509371/ |
| DOI: | https://doi.org/10.1038/s41591-026-04528-x |
Publication 21262
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The study of pre-symptomatic amyotrophic lateral sclerosis (ALS) and the design of disease prevention trials are greatly hampered by our inability to predict which unaffected carriers of ALS-associated pathogenic variants will phenoconvert to clinically manifest disease and when. In this longitudinal Olink Explore, high-throughput, proteomic study, 516 serially collected plasma samples from 33 phenoconverters, 35 patients with ALS, 10 pre-symptomatic pathogenic variant carriers and 59 controls were included. Here we identified 92 proteins with concentrations that changed before phenoconversion; characterized the longitudinal trajectory of these proteins and identified a core panel of 19 proteins which, collectively, predicted phenoconversion over the 0.5-year to 5-year time horizons (crossvalidated areas under the curve 0.80-0.89) and yielded estimates of time to phenoconversion with a mean absolute error of 1.6 years. These findings were partially replicated in UK Biobank data, confirming pre-symptomatic increases in several proteins (for example, NEFL, EDA2R and CA3) and that a multi-protein panel outperformed NEFL alone in estimating time to phenoconversion. This work sheds light on the biology of pre-symptomatic ALS. Moreover, our identification of a panel of new susceptibility/risk biomarkers based on empirical longitudinal data furthers the ultimate goal of ALS prevention.</p>
16 Authors
- Ximing Ran
- Joanne Wuu
- Zhaohui S. Qin
- Michael P. McDermott
- Johnathan Cooper-Knock
- Yindi Li
- Volkan Granit
- Anne-Laure Grignon
- Elizabeth Lin
- Maria Catalina Fernandez
- Danielle Colato
- Nathan Carberry
- Christina M. Lill
- Paolo Piazza
- Andrea Malaspina
- Michael Benatar
1 Application
| Application ID | Title |
|---|---|
| 847687 | Integrative Analysis of Genotype and Environmental/Lifestyle Factors for Individualized Disease Risk Stratification in UK Biobank |
Enabling scientific discoveries that improve human health