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Background: The International Multiple Sclerosis Genetics Consortium and MultipleMS Consortium recently reported a genetic variant associated with multiple sclerosis (MS) severity. However, it remains unclear if these variants remain associated with more robust, longitudinal measures of disease severity. Methods: We examined the top variant, rs10191329, from Harroud et al.'s study in 1813 relapse-onset MS patients from the MSBase Registry to assess association with longitudinal disease severity. Results: Our analysis revealed no significant association between rs10191329 genotype and longitudinal binary disease severity (p > 0.05). Conclusion: These findings highlight the complexity of genetic factors mediating long-term MS outcomes and the need for further research. [ABSTRACT FROM AUTHOR]

This letter responds to comments made by other researchers regarding a previous article on the genetics of multiple sclerosis severity. The authors disagree with the criticism of their power calculations and explain that their study was not a discovery project but rather aimed to validate the effect of a specific genetic variant. They also highlight differences between their study and others, including the use of a different cohort and a different definition of disease severity. The authors emphasize the complexity of the genetic association with MS progression and the need for further research using modern approaches. They conclude by inviting collaboration to replicate their findings using machine learning methodology. [Extracted from the article]

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82–0.89, p = 1.22 × 10−29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66–0.76, p = 9.07 × 10−17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS. [ABSTRACT FROM AUTHOR]

Multiple sclerosis is a leading cause of neurological disability in adults. Heterogeneity in multiple sclerosis clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international multiple sclerosis Registry, MSBase. We assembled a cohort of deeply phenotyped individuals of European ancestry with relapse-onset multiple sclerosis. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined multiple sclerosis severity phenotypes in 1,813 individuals. Our primary analyses did not identify any genetic variants of moderate to large effect sizes that met genome-wide significance thresholds. The strongest signal was associated with rs7289446 (β=-0.4882, P = 2.73 × 10-7), intronic to SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in relapse-onset multiple sclerosis are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62,000 variants together with clinical and demographic variables available at multiple sclerosis disease onset, we could predict severity with an area under the receiver operator curve of 0.84 (95% CI 0.79-0.88). Our machine learning algorithm achieved positive predictive value for outcome assignation of 80% and negative predictive value of 88%. This outperformed our machine learning algorithm that contained clinical and demographic variables alone (area under the receiver operator curve 0.54, 95% CI 0.48-0.60). Secondary, sex-stratified analyses identified two genetic loci that met genome-wide significance thresholds. One in females (rs10967273; βfemale =0.8289, P = 3.52 × 10-08), the other in males (rs698805; βmale = -1.5395, P = 4.35 × 10-08), providing some evidence for sex dimorphism in multiple sclerosis severity. Tissue enrichment and pathway analyses identified an overrepresentation of genes expressed in central nervous system compartments generally, and specifically in the cerebellum (P = 0.023). These involved mitochondrial function, synaptic plasticity, oligodendroglial biology, cellular senescence, calcium and g-protein receptor signalling pathways. We further identified six variants with strong evidence for regulating clinical outcomes, the strongest signal again intronic to SEZ6L (adjusted hazard ratio 0.72, P = 4.85 × 10-4). Here we report a milestone in our progress towards understanding the clinical heterogeneity of multiple sclerosis outcomes, implicating functionally distinct mechanisms to multiple sclerosis risk. Importantly, we demonstrate that machine learning using common single nucleotide variant clusters, together with clinical variables readily available at diagnosis can improve prognostic capabilities at diagnosis, and with further validation has the potential to translate to meaningful clinical practice change. [ABSTRACT FROM AUTHOR]

Background: People with multiple sclerosis and neuroimmunologic disorders (herein referred to as patients) are increasingly treated with infusible monoclonal antibodies. This rise in demand has placed increased loads on current infusion services and mandates careful strategic planning. This study examined patient preferences for the timing and location of infusions and their association with demographic and disease variables to facilitate patient-focused strategic planning. Methods: Ninety-one patients receiving an infusible therapy at an infusion service during March 2019 were asked to complete a questionnaire exploring eight domains, including preferences for time of infusions and location of infusion centers. Potential access to home-based treatment was included as an option. Unstructured (free-text) feedback on current service was also obtained. Results: Eighty-three patients completed the survey (mean age, 42 years; 75% women). Infusions were predominantly natalizumab (66%) and ocrelizumab (25%). Of these patients, 71% were engaged in some form of work or study, and 83% of this group had to arrange time off from work or study to attend treatment. Seventy percent of patients would prefer their infusion before noon, and 60% would consider home-based infusions. Most used a car as their transport to the infusion service. Conclusions: These results suggest that patients are more likely to prefer infusible treatment in the morning and are open to home-based infusions. This study provides information for health services to target service delivery at peak preference times and consider alternate ways of delivering infusible treatments. [ABSTRACT FROM AUTHOR]