Increased ability to predict disease course and response to disease-modifying therapies in multiple sclerosis (MS) would optimise treatment outcomes by guiding selection of patients for a particular therapeutic intervention. Several factors affecting disease progression have been identified, including individual characteristics such as age at onset and race, onset of symptoms, early disease outcomes and radiological measures. While studies of magnetic resonance imaging (MRI) prognostic indicators have given mixed results, advances in technology are increasing the predictive power of MRI, and new techniques and outcome measures are providing alternative means of predicting disease course and response to treatment. The search for a predictive biomarker is an area of active research but studies remain poorly validated. Potential biomarkers include neurofilament proteins, microRNAs, gene expression and antibodies. Since it is unlikely that a single factor may predict disease course, a number of composite scoring systems have been proposed, but none have yet received widespread acceptance. However, it seems likely that in the future, a combination of MRI and biochemical biomarkers will provide a foundation for therapeutic decision-making in MS allowing an individualised approach.
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