Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis
Abstract
Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most
patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but
not all patients with this syndrome develop multiple sclerosis over time, and currently, there is no clinical test that can
conclusively establish whether a patient with a clinically isolated syndrome will eventually develop clinically defined multiple
sclerosis. Here, we took advantage of the capabilities of targeted mass spectrometry to establish a diagnostic molecular classifier
with high sensitivity and specificity able to differentiate between clinically isolated syndrome patients with a high and
a low risk of developing multiple sclerosis. Based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase
in cerebrospinal fluid, we built a statistical model able to assign to each patient a precise probability of conversion to
clinically defined multiple sclerosis. Our results are of special relevance for patients affected by multiple sclerosis as
early treatment can prevent brain damage and slow down the disease progression.
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