Objective:To investigate pregnancy-related disease activity in a contemporary multiple sclerosis (MS) cohort.Methods:Using data from the MSBase Registry, we included pregnancies conceived after 31 Dec 2010 from women with relapsing-remitting MS or clinically isolated syndrome. Predictors of intrapartum relapse, and postpartum relapse and disability progression were determined by clustered logistic regression or Cox regression analyses.Results:We included 1998 pregnancies from 1619 women with MS. Preconception annualized relapse rate (ARR) was 0.29 (95% CI 0.27-0.32), fell to 0.19 (0.14-0.24) in third trimester, and increased to 0.59 (0.51-0.67) in early postpartum. Among women who used fingolimod or natalizumab, ARR before pregnancy was 0.37 (0.28-0.49) and 0.29 (0.22-0.37), respectively, and increased during pregnancy. Intrapartum ARR decreased with preconception dimethyl fumarate use. ARR spiked after delivery across all DMT groups. Natalizumab continuation into pregnancy reduced the odds of relapse during pregnancy (OR 0.76 per month [0.60-0.95], p=0.017). DMT re-initiation with natalizumab protected against postpartum relapse (HR 0.11 [0.04-0.32], p<0.0001). Breastfeeding women were less likely to relapse (HR 0.61 [0.41-0.91], p=0.016). 5.6% of pregnancies were followed by confirmed disability progression, predicted by higher relapse activity in pregnancy and postpartum.Conclusion:Intrapartum and postpartum relapse probabilities increased among women with MS after natalizumab or fingolimod cessation. In women considered to be at high relapse risk, use of natalizumab before pregnancy and continued up to 34 weeks gestation, with early re-initiation after delivery is an effective option to minimize relapse risks. Strategies of DMT use have to be balanced against potential fetal/neonatal complications.

IMPORTANCE Multiple sclerosis (MS) is usually diagnosed in women during their childbearing years. Currently, no consensus exists on whether pregnancy can delay the first episode of demyelination or clinically isolated syndrome (CIS).OBJECTIVE To investigate the association of pregnancy with time to CIS onset. DESIGN, SETTING, AND PARTICIPANTSThis multicenter cohort study collected reproductive history (duration of each pregnancy, date of delivery, length of breastfeeding) on all participants between September 1, 2016, and June 25, 2019. Adult women being treated at the MS outpatient clinics of 4 tertiary hospitals in 2 countries (

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.

ObjectiveTo determine whether serum neurofilament light chain (sNfL) levels are associated with recent MRI activity in patients with relapsing-remitting MS (RRMS).MethodsThis observational study included 163 patients (405 samples) with early RRMS from the Study of Early interferon-beta1a (IFN-β1a) Treatment (SET) cohort and 179 patients (664 samples) with more advanced RRMS from the Genome-Wide Association Study of Multiple Sclerosis (GeneMSA) cohort. Based on annual brain MRI, we assessed the ability of sNfL cutoffs to reflect the presence of combined unique active lesions, defined as new/enlarging lesion compared with MRI in the preceding year or contrast-enhancing lesion. The probability of active MRI lesions among patients with different sNfL levels was estimated with generalized estimating equations models.ResultsFrom the sNfL samples ≥90th percentile, 81.6% of the SET (OR = 3.4, 95% CI = 1.8-6.4) and 48.9% of the GeneMSA cohort samples (OR = 2.6, 95% CI = 1.7-3.9) was associated with radiological disease activity on MRI. The sNfL level between the 10th and 30th percentile was reflective of negligible MRI activity: 1.4% (SET) and 6.5% (GeneMSA) of patients developed ≥3 active lesions, 5.8% (SET) and 6.5% (GeneMSA) developed ≥2 active lesions, and 34.8% (SET) and 11.8% (GeneMSA) showed ≥1 active lesion on brain MRI. The sNfL level <10th percentile was associated with even lower MRI activity. Similar results were found in a subgroup of clinically stable patients.ConclusionsLow sNfL levels (≤30th percentile) help identify patients with MS with very low probability of recent radiologic disease activity during the preceding year. This result suggests that in future, sNfL assessment may substitute the need for annual brain MRI monitoring in considerable number (23.1%–36.4%) of visits in clinically stable patients.