Inflammatory mediators, lipoproteins and apolipoproteins in early diagnosis of amyotrophic lateral sclerosis.

SLAS Technol

Laboratoire de biochimie et biologie moléculaire, CHRU Bretonneau, 2 Boulevard Tonnellé, 37000 Tours, France; UMR 1253 iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37000 Tours, France.

Published: October 2022

AI Article Synopsis

  • There are currently no available biomarkers for diagnosing or predicting Amyotrophic Lateral Sclerosis (ALS) in clinical settings, leading researchers to explore the potential of multiple inflammatory markers, lipids, and apolipoproteins.
  • In a study of 25 ALS patients and 23 controls, various blood tests revealed significant differences, allowing for high accuracy (94%) in distinguishing between the groups using advanced statistical analysis methods.
  • While the predictive accuracy was promising, the study's models could not differentiate between subgroups of ALS patients, highlighting the importance of exploring multiple biological pathways rather than focusing on a single biomarker.

Article Abstract

There is currently no diagnostic or prognostic biomarker available in clinical practice for Amyotrophic Lateral Sclerosis (ALS). The objective of this study was to monitor a combination of various inflammatory markers, lipids, and apolipoproteins alterations in ALS patients at the time of diagnosis, to assess their role as early diagnostic or prognostic biomarker candidates. C-reactive protein, orosomucoid, prealbumin, calprotectin, lipids and apoliproteins were determined in the blood of all subjects (25 ALS patients, 23 controls) as routinely performed in our laboratory. Inflammatory mediators were evaluated by a bead-based multiplex assay. A two-step approach was used for each analytical strategy: univariate analysis followed by multivariate analysis. Eight features were significantly different between ALS patients and controls, sometimes with important fold changes. The supervised Partial least Squares Discriminant Analysis separated ALS and controls with great accuracy (94 %) and the permutation test was significant (p < 0.01), ensuring the robustness of the model. The prediction model leads to a mean sensitivity and specificity of 90 (+/- 10) and 78 (+/- 10) %, respectively, with a mean predictive positive value and negative predictive value of 80 (+/- 8.9) and 89 (+/- 11.8) %, respectively. However, the models did not discriminate subgroups of ALS patients based on ALS characteristics. This study highlights the usefulness of evaluating a combination of multiple pathways rather than focusing on a single target. These promising results suggest the need for the longitudinal monitoring of these candidates to determine their role in disease evolution.

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Source
http://dx.doi.org/10.1016/j.slast.2022.07.003DOI Listing

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