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Using biomarkers to predict clinical outcomes in multiple sclerosis

Castle, Daniel, Wynford-Thomas, Ray, Loveless, Sam, Bentley, Emily, Howell, Owain W and Tallantyre, Emma C 2019. Using biomarkers to predict clinical outcomes in multiple sclerosis. Practical Neurology 19 (4) , pp. 342-349. 10.1136/practneurol-2018-002000

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Abstract

Long-term outcomes in multiple sclerosis (MS) are highly varied and treatment with disease-modifying therapies carries significant risks. Finding tissue biomarkers that can predict clinical outcomes would be valuable in individualising treatment decisions for people with MS. Several candidate biomarkers—reflecting inflammation, neurodegeneration and glial pathophysiology—show promise for predicting outcomes. However, many candidates still require validation in cohorts with long-term follow-up and evaluation for their independent contribution in predicting outcome when models are adjusted for known demographic, clinical and radiological predictors. Given the complexity of MS pathophysiology, heterogeneous panels comprising a combination of biomarkers that encompass the various aspects of neurodegenerative, glial and immune pathology seen in MS, may enhance future predictions of outcome.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher: BMJ Publishing Group / Blackwell Publishing
ISSN: 1474-7758
Date of First Compliant Deposit: 12 July 2019
Date of Acceptance: 25 March 2019
Last Modified: 16 Jul 2019 09:12
URI: http://orca-mwe.cf.ac.uk/id/eprint/124194

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