Updated project metadata. The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need to identify reliable biomarkers to monitor disease progression, therapeutic response and classify patients according to disease severity. Objectives of this study were to identify potential biomarkers for disease severity, and to describe changes in the proteomic profile after 302 days of nusinersen administration (T302). In this multicenter retrospective longitudinal study, we employed an untargeted non-targeted mass spectrometry-based proteomic approach (LC-MS) on cerebrospinal fluid (CSF) samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at baseline and T302. A machine learning classifier approach (Random Forest, RF) was applied to exploit proteins able to stratify disease severity at baseline. Bioinformatics analysis was performed to investigate Gene Ontology (GO) functional annotation of differentially expressed proteins (DEPs) at T302. The RF algorithm identified CNTN1 and NRXN3 as new potential biomarkers of disease severity based on their expression at baseline. Analysis of changes in proteomic profiles identified 147 DEPs after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3. Overall, Nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in all SMA types (i.e. regulation of axonogenesis), and ii) disease severity-specific changes, namely regulation of glucose metabolism in SMA1, of coagulation processes in SMA2, and of complement cascade in SMA3. By analyzing a large cohort of CSF samples from SMA patients, and applying cutting-edge bioinformatic analysis and artifical intelligence alghorithms, this study has identified new potential biomarkers of disease severity, and provided new insights on biological processes modulation after 302 days of nusinersen treatment.