Disease phenotype prediction in multiple sclerosis

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Published: 2023-06-19

Formatted citation

Herman S, Arvidsson McShane S, Zjukovskaja C, Khoonsari PE, Svenningsson A, Burman J, Spjuth O, Kultima K.. Disease phenotype prediction in multiple sclerosis.
iScience. (2023). DOI: 10.1016/j.isci.2023.106906

Abstract

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (nxa0= 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUCxa0= 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.