Multiple Sclerosis (MS) is a chronic, autoimmune disorder of the central nervous system, a leading cause of non-traumatic disability in young adults. It is a very heterogeneous disease, with high variability in both clinical manifestations and individual response to treatments, suggesting that specific individual characteristics could play a role and be implicated in disease expression. Starting an effective treatment as early as possible is extremely important, to reduce inflammatory activity and to limit disability progression. This aspect is even more relevant in the present era, characterized by a dramatic increase in the range of treatment options currently available for MS patients.

We plan to study a large cohort of Italian and French patients, combining together clinical, molecular (e.g. genetics, gene expression, methylation) and life-style data. This composite information will be used to stratify patients in subgroups, using advanced bioinformatics tools. These approaches, together with artificial intelligence models, will be used to identify biomarkers able to predict disease activity, with the final aim of supporting treatment choice in the early phases of the disease. Overall we estimate that this project is clinically relevant and could have a positive impact in MS patient’s management, towards a more tailored use of available treatment options. We expect that this project will contribute to produce personalized medicine tools, potentially applicable in the future in the clinical setting.