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August 19, 2025Nature Medicine37 citationsOpen Access

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

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RCRuth ChiaRMRuin MoaddelJKJustin Kwan

Key Points

  • The model diagnosed ALS with an accuracy of 98.3%, highlighting the potential of plasma proteins as biomarkers.
  • Thirty-three proteins were different in ALS patients compared to controls, providing critical insights into disease mechanisms.
  • This cross-sectional analysis involved 183 patients with ALS and 309 controls to assess plasma proteomics.
  • Findings suggest the disease may impact skeletal muscle and metabolism years before symptoms appear, indicating early opportunities for intervention.

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.

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Cite This Study

Chia et al. (2025) studied this question.

synapsesocial.com/papers/68af4551ad7bf08b1ead37achttps://doi.org/10.1038/s41591-025-03890-6
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