1. Using biomarkers to predict clinical outcomes in multiple sclerosis.
- Author
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Castle, Daniel, Wynford-Thomas, Ray, Loveless, Sam, Bentley, Emily, Howell, Owain W., and Tallantyre, Emma C.
- Subjects
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MULTIPLE sclerosis treatment , *TISSUE analysis , *DISEASE risk factors , *NEURODEGENERATION , *INFLAMMATION , *BIOMARKERS , *MULTIPLE sclerosis , *NEUROGLIA , *DECISION making in clinical medicine , *TREATMENT effectiveness - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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