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Journal of Neuroinflammation
Published in: Journal of Neuroinflammation 1/2024

Open Access 01-12-2024 | Multiple Sclerosis | Research

Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

Authors: Eleftheria Kodosaki, W. John Watkins, Sam Loveless, Karim L. Kreft, Aidan Richards, Valerie Anderson, Lisa Hurler, Neil P. Robertson, Wioleta M. Zelek, Emma C. Tallantyre

Published in: Journal of Neuroinflammation | Issue 1/2024

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Abstract

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts.
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Metadata
Title
Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes
Authors
Eleftheria Kodosaki
W. John Watkins
Sam Loveless
Karim L. Kreft
Aidan Richards
Valerie Anderson
Lisa Hurler
Neil P. Robertson
Wioleta M. Zelek
Emma C. Tallantyre
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Journal of Neuroinflammation / Issue 1/2024
Electronic ISSN: 1742-2094
DOI
https://doi.org/10.1186/s12974-024-03036-4

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