A plasma proteomics-based candidate biomarker panel predictive of amyotrophic lateral sclerosis

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.

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Publication Details

Journal
Nature Medicine
Published
2025-08-19
DOI
https://doi.org/10.1038/s41591-025-03890-6
Citations
10
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
13.89

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article

A plasma proteomics-based candidate biomarker panel predictive of amyotrophic lateral sclerosis

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10 citations
Nature Medicine
Amyotrophic Lateral Sclerosis Research
13.89
article

A plasma proteomics-based candidate biomarker panel predictive of amyotrophic lateral sclerosis

Josephine Mary Egan, Min Zhu, J. Raphael Gibbs, Péter Kósa, Derek P. Narendra, Clifton L. Dalgard, Anindita Ray, Ruin Moaddel, Antonio Canosa, Ruth Chia, Ted M. Dawson, Bryan J. Traynor, Paola Ruffo, Alexander Pantelyat, Sonja W. Scholz, Luigi Ferrucci, Andrea Calvo, Natalie Landeck, Liana S. Rosenthal, Umberto Manera, Maurizio Grassano, Sara Sáez-Atiénzar, Julián Candia, Rosario Vasta, Debra J. Ehrlich, Keenan A. Walker, Toshiko Tanaka, Bibiana Bielekova, Anna J. Hall, Memoona Rasheed, Paolo Reho, Adriano Chiò, Arvind Kumar, Allison Snyder, Federico Casale, Justin Kwan, Cristina Moglia, Maura Brunetti, Betul Comertpay
article en
10 citations

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.

Nature MedicineVol. 31(10)
Henry M. Jackson Foundation (US), National Institutes of Health (US), Johns Hopkins University (US), Uniformed Services University of the Health Sciences (US), Johns Hopkins Medicine (US), Amyotrophic Lateral Sclerosis Association (US), Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT), National Institute of Neurological Disorders and Stroke (US), National Institute of Allergy and Infectious Diseases (US), National Institute on Aging (US), National Center for Advancing Translational Sciences (US), Institute of Cognitive Sciences and Technologies (IT), The Ohio State University (US), Columbia University (US)
Ministero della Salute, Uniformed Services University of the Health Sciences, National Institute on Aging, National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 1%
Amyotrophic Lateral Sclerosis Research
13.89
DOIOpen Access PDFOpenAlex
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