Marzena Pedrini

Introduction: Timely identification of the transition from relapsing-remitting multiple sclerosis (RRMS) to secondary- progressive MS (SPMS) is of great weight for effective treatment planning. However, this transition is typically diagnosed with an average delay of three years, leading to missed opportunities for early intervention. Building on our previous artificial intelligence (AI) work with Swedish electronic health record data, we extend our approach to the global MSBase cohort. Objectives/Aims: To develop AI models that predict disease progression from RRMS to SPMS proactively in a globally heterogeneous MS population while enabling user-defined confidence levels and interpretable predictions. Methods: We utilized two large-scale MS registries: MSBase (110,000 patients; 1.3 million visits across 45 countries) and the Swedish MS registry (22,000 patients; 200,000 visits). We trained random forest classifiers to predict disease states at each clinical encounter, integrating conformal prediction to quantify predictive uncertainty and explainable AI to enhance interpretability and transparency. Results: The global model achieved an F1 score of 0.83 and outperformed country-specific models in several regions. However, in certain countries, local models were better fitted. Calibration curves revealed marked differences in RRMS and SPMS diagnoses across countries. We identified groups with aligned predictions by clustering countries based on calibration similarity. While the global model generalized well, clustered models improved local accuracy. Conclusion: We developed AI models that provide accurate and interpretable predictions of MS progression trained on registry data from 18 countries. The global model offers scalability, while localized approaches better capture regional diagnostic practices. This framework supports harmonizing diagnostic standards and can enhance clinical trial design and international data interpretation.

Introduction: The study includes the latest findings from the MSBase Registry on the real-world Australian experience with Ofatumumab (OFA) in the treatment of relapsing multiple sclerosis (RMS). Here, we report the latest patient demographics, disease characteristics, and prior therapy history, providing valuable insights into the clinical efficacy and treatment persistence of OFA in both treatment-naive patients and those who have switched from prior therapies. Objectives/Aims: To continue to characterize the use of OFA in Australia through an evaluation of patient demographics, baseline characteristics and prior therapy of patients initiating OFA as recorded in MSBase. In addition, evaluations of clinical efficacy included annual relapse rate and estimates of time to first clinical event. Methods: This is the second analysis of a retrospective, secondary use of data study from the MSBase Registry, describing the baseline characteristics of RMS patients in Australia initiated on OFA treatment. Analyses included demographics, expanded disability status scale (EDSS) and treatment history with diseasemodifying therapies (DMTs). Kaplan Meier estimates were calculated for persistence, time to EDSS milestone and confirmed disability worsening. Results: As of 1st March 2025, MSBase has included 605 Australian patients who received at least one dose of ofatumumab, with a median age of 42.25 (35.86, 50.73) years and 76.7% female. The median disease duration was 8.75 (3.21, 16.43) years, and 22.3% of the participants were treatmentnaive. The most common previous DMTs included ocrelizumab (26.9%) and natalizumab (16.4%). The annualized relapse rate (ARR) during the follow-up period was significantly reduced to 0.05 (0.04, 0.07) compared to 0.34 (0.29, 0.40) in the year prior to starting ofatumumab (p<0.0001). Additionally, the change in EDSS from baseline indicated stable disability scores with median change 0 (-0.5, 0), 0 (-0.5, 0.5) and 0 (-0.5, 0.5) at 12, 18 and 24 months respectively. Kaplan Meier estimates calculated treatment persistence at 96.6% [0.9441, 0.9795] at one year and 93.3% [0.8999, 0.9553] at two years follow-up. Conclusion: The second analysis of the real-world data from the MSBase registry provides further insight into the Australian experience of relapsing MS patients initiated on OFA, with a high treatment persistence and stable disability over 24 months on treatment.

Introduction: The major histocompatibility complex (MHC) locus carries a significant genetic risk burden for MS, though within the MHC the structurally diverse complement component 4 (C4) alleles remain largely understudied. Objectives/Aims: To investigate C4 genetic structural variations in MS risk and better understand how the MHC region shapes disease susceptibility. Methods: We used an established protocol to impute and analyse C4 alleles based on available genotyping data from two case-control cohorts (N1= 3252 cases and 5725 controls; N2= 8978 cases and 6976 controls), a clinical MS cohort (N3= 2387 cases) and a cohort with immune cell expression data (N4= 33 cases and 33 controls). We also performed gene-level analysis to examine the shared genetic landscape between MS onset (NGWAS= 14802 cases and 26703 controls) and plasma C4 protein (NGWAS= 68716). Results: Our data showed that C4 genetic structural variants were associated with significant changes in the risk of development of MS and the progression of MS. For instance, higher C4AL copy number burden was associated with lower risk of MS onset (fixed effect meta-analysis odds ratio= 0.89, P= 5.65×10-6) and reduced hazard of reaching MS disability milestones such as Expanded Disability Status Scale 3 (hazard ratio= 0.79, P= 9.0x10-15). In downstream gene expression analysis, we found C4 alleles may also modulate C4 expression in diseaserelevant immune cell types such as CD8+ T cells. Further, we identified that candidate genes shared between MS onset risk and plasma C4 protein level were enriched in biological pathways of immune regulation, Epstein-Barr virus infection and other autoimmune diseases such as lupus. Conclusion: These findings support future investigations of the C4 genetic structural variants as potential mechanistic and therapeutic targets in MS pathogenesis and disease progression.

Introduction: Serum neurofilament light chain (sNfL) has become a promising biomarker for acute and chronic neuroaxonal damage in relapsing and progressive multiple sclerosis (MS). Evidence is accumulating that sNfL levels have a predictive value for the risk of disability progression in MS. Objectives/Aims: To assess serum neurofilament light chain (sNfL) levels in patients with progressive MS. Methods: We analysed sNfL levels in a cross-sectional cohort study involving 181 patients with progressive MS (PMS). Additionally, we investigated the correlation between disease activity and sNfL levels in 341 patients with relapsing MS (RMS) using single molecule array technology. Results: Serum NfL levels (pg/mL) were significantly elevated in patients with RMS and PMS compared to HC (29.4 vs 12.3, p<0.05; 38.4 vs 12.3, p<0.001, respectively). Additionally, higher sNfL levels were correlated with disability. A statistically significant difference in sNfL levels was observed between relapsing and progressive disease in individuals not receiving treatment (35.2 vs 42.0, p<0.01, adjusted for age). Furthermore, treatment significantly reduced sNfL levels in RMS compared to those not receiving treatment (24.4 vs 36.9, p<0.01, adjusted for age). Notably, a statistically significant difference was observed only with natalizumab and fingolimod, but not with interferons or glatiramer acetate when compared to individuals not receiving treatment. Conclusion: The findings support the utility of sNfL measurement to monitor MS disease activity and progression alongside magnetic resonance imaging.