Disease phenotype prediction in multiple sclerosis

← Back to publications

Published: 2023-06-09

Formatted citation

Herman S, Arvidsson McShane S, Zhukovsky C, Khoonsari PE, Svenningsson A, Burman J, Spjuth O, Kultima K.. Disease phenotype prediction in multiple sclerosis.
iScience. 26, 6 (2023). DOI: 10.1016/j.isci.2023.106906


Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we investigated an alternative route to the diagnosis. A selection of cerebrospinal fluid metabolites (n=15) were shown to be able to distinguish PMS from relapsing-remitting MS (AUC=0.93) in a validation cohort. Conformal prediction models showed that, at a 94% confidence level, 88% of patients could be correctly predicted, and four out of eight patients that developed PMS within three years were predicted as PMS. The methodology was further applied to PMS patients in a clinical trial where traditional endpoints were largely inconclusive. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. Also on a group-level, a significant (P<0.01) biochemical effect could be seen. This conceptual work demonstrates a methodology that may be useful in monitoring disease progression and as an endpoint in future trials. Finally, 4-acetamidobutanoate showed significant associations to several clinical measures, further strengthening the evidence of its relation to MS.