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Multiple sclerosis (MS) is a complex inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS), and is one of the leading causes of non-traumatic disability in young adults. There is a critical need for biomarkers that reflect the underlying pathological mechanisms driving the considerable heterogeneity observed across different MS disease courses and that can be readily monitored. Identifying such biomarkers would enable clinicians to make more informed decisions regarding personalized treatment strategies and would support the development of new therapies.

Conventional magnetic resonance imaging (MRI) remains a cornerstone for both diagnosis and disease monitoring in MS. However, it lacks the sensitivity required to detect and differentiate the complex pathology underlying the diverse clinical manifestations of the disease. Consequently, there is an urgent need to discover novel biomarker candidates.

An exploratory and translational strategy to identify new biomarkers in plasma, cerebrospinal fluid (CSF), and via MRI will generate large and complex datasets, necessitating the use of machine-learning approaches for data analysis. Building on our previous work employing this methodology, we aim to further expand these targeted proteomic analyses and integrate them with molecular information from microRNAs (miRNAs) and data obtained from advanced quantitative MRI (qMRI) techniques, with the goal of identifying biomarkers that can guide personalized treatment in MS.