A plasma proteomics-based candidate biomarker panel predictive of amyotrophic lateral sclerosis
Ruth Chia, Ruin Moaddel, Justin Kwan, Memoona Rasheed, Paola Ruffo, Natalie Landeck, Paolo Reho, Rosario Vasta, Andrea Calvo, Cristina Moglia, Antonio Canosa, Umberto Manera, Allison Snyder, Sara Sáez-Atiénzar, Maurizio Grassano, Maura Brunetti, Federico Casale, Anindita Ray, Arvind Kumar, Betul Comertpay, Min Zhu, J. Raphael Gibbs, Camille Alba, Ted M. Dawson, Liana S. Rosenthal, Anna J. Hall, Alexander Pantelyat, Derek P. Narendra, Debra Ehrlich, Keenan A. Walker, Péter Kósa, Bibiana Bielekova, Josephine M. Egan, Julián Candia, Toshiko Tanaka, Luigi Ferrucci, Clifton L. Dalgard, Sonja W. Scholz, Adriano Chiò, Bryan J. Traynor
Abstract
Identifying a reliable biomarker for amyotrophic lateral sclerosis (ALS) is crucial for clinical practice. Here, in this cross-sectional study, we used the Olink Explore 3072 platform to investigate plasma proteomics as a biomarker tool for this neurodegenerative condition. Thirty-three proteins were differentially abundant in the plasma of patients with ALS (n = 183) versus controls (n = 309). We replicated our findings in an independent cohort (n = 48 patients with ALS and n = 75 controls). We then applied machine learning to create a model that diagnosed ALS with high accuracy (area under the curve, 98.3%). By analyzing plasma samples from individuals before ALS symptoms emerged, we estimated the age of clinical onset and showed that the disease process-impacting skeletal muscle, nerves and energy metabolism-occurs years before symptoms appear. Our research suggests that plasma proteins can be a biomarker for this fatal disease and offers molecular insights into its prodromal phase.