Background

Treatment options for pediatric patients with relapsing forms of multiple sclerosis (RMS) are limited. Teriflunomide, approved for adults with RMS in >80 countries, was investigated in pediatric RMS in TERIKIDS (NCT02201108), a 2-year, multicenter, multinational, randomized, double-blind (DB), placebo-controlled, parallel-group phase 3 study.

Objectives

To report the interim results in pediatric patients from the open-label (OL) period of the TERIKIDS study as of 27 November 2019.

Methods

Patients who either completed 96-week DB treatment or qualified for early switch from DB treatment to OL teriflunomide could continue in the OL period until 192 weeks after initial randomization. All patients in the OL period received teriflunomide at a dose based on body weight, equivalent to 14 mg in adults.

Results

In the DB period, teriflunomide reduced risk of relapse (−34%); however, the difference was not statistically significant versus placebo (P=0.29) so TERIKIDS did not meet its primary endpoint. Teriflunomide significantly reduced risk of relapse or high MRI activity (−43%; P=0.041; prespecified sensitivity analysis), number of new/enlarging T2 lesions (−55%; P=0.0006), and number of gadolinium-enhancing lesions (−75%; P<0.0001) relative to placebo. At the cut-off date, 100 (91.7%) patients from the teriflunomide and 52 (91.2%) from the placebo group enrolled in the OL period; 34 patients discontinued, 30 completed, and 88 were ongoing. From DB randomization to week 192, risk of relapse was numerically lower with continuous teriflunomide versus placebo/teriflunomide (hazard ratio [95% CI]: 0.61 [0.38 to 0.98]; P=0.098), as was risk of disability progression sustained for 24 weeks (hazard ratio [95% CI]: 0.552 [0.245 to 1.242]). Number of new/enlarging T2 lesions per MRI scan was reduced with continuous teriflunomide versus placebo/teriflunomide (6.3 vs 13.0; P=0.0006). Incidence of adverse events during the OL period was lower with continuous teriflunomide versus placebo/teriflunomide (68.0% vs 82.7%). Adverse events led to treatment discontinuation during the OL period in 8 patients (increased alanine aminotransferase [n=5], peripheral neuropathy [n=1], pancreatitis [n=2]).

Conclusions

Interim analysis showed that continuous teriflunomide numerically lowered the risk of clinical relapses and 24-week sustained disability progression in pediatric patients compared with delayed initiation of teriflunomide after placebo. Teriflunomide was well tolerated and had a manageable safety profile.

STUDY SUPPORT: Sanofi.

Background

White matter (WM) microstructural changes occur in youth with multiple sclerosis (MS) and myelin oligodendrocyte glyoprotein (MOG)-associated disorders. While diffusion tensor imaging has been extensively used to characterize white matter, this method lacks microstructural and pathological specificity. ‘Fixel Based Analysis’ (FBA) statistically estimates changes in diffusion MRI connectivity that is specific to micro and macro-structure. WM damage that leads to less densely packed axons in a fiber bundle causes a decrease in fibre density (FD). If the number of axons is not reduced but occupies less area, then fibre cross-section (FC) will decrease. Last, if the density of axons within a fibre bundle and the area the bundle occupies are reduced, then fibre density and cross-section (FDC) will decrease.

Objectives

To use whole-brain FBA to measure differences in FD, FC, FDC in youth with demyelinating syndromes compared to healthy controls.

Methods

We evaluated group differences in the FBA metrics between 28 typically developing children (17F; age 15.0±2.6y), 19 children with MS (13F; 16.9±1.1y; disease duration (DD)=0.1-11.7y; expanded disability status scale(EDSS):median=1.5,range=0-4.5), and 11 children with MOG (8F;12.1±2.8y; DD=0.5-6.4y;EDSS:m=1.0,r=0-3). Multi-shell diffusion-weighted imaging of the brain was acquired with echo planar imaging on a 3T MRI scanner and was pre-processed to correct for distortions and movement. Whole-brain group FBA was performed on FD, FC and FDC to test differences between groups adjusting for age, sex, total intracranial volume, EDSS and DD (p<0.05, family-wise error (FWE) corrected).

Results

Participants with MS and MOG showed reduced FD, FC and FDC relative to typically developing children (FWE corrected p<0.05). Differences in FD were found within splenium, superior longitudinal fasciculus and optic radiations. MS patients had reduced FDC within the corticospinal tract and cerebellar peduncle compared to MOG patients. In participants with MS and MOG, decreased FD within the brain stem, cerebellar peduncles and corona radiata was associated with increased DD and EDSS.

Conclusions

Our preliminary findings showed that patients with demyelinating disorders display decreased axonal density and fibre bundle size in multiple WM tracts relative to typically developing children, which were related to clinical outcomes (EDSS, DD). These changes were more pronounced in MS compared to MOG participants in selected WM tracts.

Background

Metagenomic sequencing reveals the functional potential of the gut microbiome, and may explain how the gut microbiome influences pediatric-onset multiple sclerosis (MS) risk.

Objectives

To examine the gut microbiome functional potential by metagenomic analysis of stool samples from pediatric MS cases and controls using a case-control design.

Methods

Persons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network who provided a stool sample and were not exposed to antibiotics or corticosteroids 30 days prior were included for study. All MS cases met McDonald criteria, had symptom onset <18 years of age and had either no prior disease-modifying drug (DMD) exposure or were exposed to beta-interferon or glatiramer acetate only. Twenty MS cases were matched to 20 non-affected controls by sex, age (± 3 years), stool consistency (Bristol Stool Scale, BSS) and, when possible, by race. Shotgun metagenomic reads were generated using the Illumina NextSeq platform and assembled using MEGAHIT. Metabolic pathway analysis was used to compare the gut microbiome between cases and controls, as well as cases by DMD status (DMD naïve vs DMD exposed MS cases vs controls). Gene ontology classifications were used to assess α-diversity and differential abundance analyses (based on the negative binomial distribution) reported as age-adjusted log-fold change (LFC) in relative abundance, 95% confidence intervals (CI), and false discovery rate adjusted p-values.

Results

The MS cases were aged 13.6 mean years at symptom onset. On average, MS cases and controls were 16.1 and 15.4 years old at the time of stool collection and 80% of each group were girls. MS cases and controls were similar for body mass index (median: 22.8 and 21.0, respectively), stool consistency (BSS types 1-2: n=4, types 3-5: n=16, for both MS and controls) and race (Caucasian: 11 and 9, respectively). Eight MS cases were DMD naïve. Richness of gene ontology classifications did not differ by disease status or DMD status (all p>0.4). However, differential analysis of metabolic pathways indicated that the relative abundance of tryptophan degradation (via the kynurenine pathway; LFC 13; 95%CI: 8–19; p<0.0005) and cresol degradation (LFC 19; 95%CI: 13–25; p<0.0001) pathways were enriched for MS cases vs controls. Differences by DMD status were also observed, e.g., choline biosynthesis was enriched in DMD exposed vs naïve MS cases (LFC 21; 95%CI: 12–29; p<0.0001).

Conclusions

We observed differences in the functional potential of the gut microbiome of young individuals with MS relative to controls at various metabolic pathways, including enrichment of pathways related to tryptophan and metabolism of industrial chemicals. DMD exposure affected findings, with enrichment of pathways involved in promoting CNS remyelination (e.g., choline).

Background

Predicting diagnosis in youth at the first episode of demyelination is feasible in some but not all cases. Machine learning classifiers (MLC) can be trained to identify relationships between numerous multimodal input features and disease classifications to provide highly accurate predictions.

Objectives

To assess performance of machine learning classifiers for early disease diagnosis based on visual metrics in youth with demyelination.

Methods

Standardized clinical and visual data was prospectively collected at disease onset from 141 pediatric subjects with acquired demyelinating syndromes (ADS) and 75 healthy controls (HC). Participants were recruited through The Hospital for Sick Children (Toronto, Ontario (2010-2020)) and University of Calgary (2010-2017). Patients were classified using consensus definitions of demyelinating disorders and serum antibody testing for myelin oligodendrocyte glycoprotein (MOG) and aquaporin 4 (AQP4). Twenty-two auto-segmented Optical Coherence Tomography (OCT) features, 4 functional visual and 4 clinical features were used in a stratified manner alone or in combination to identify which combination of features provided the highest predictive accuracy. These input features were analyzed using 9 supervised MLC (Random Forest (RF), AdaBoost, XGBoost, Decision Tree (DT), Logistic Regression, Support Vector Machines (SVM), k-Nearest Neighbors, Stochastic Gradient Descent, Multinomial Naive Bayes). Data was split 80/20 between training and test sets. Backward feature selection was performed to re-run the analysis with best scoring predictor features in the MLC with highest predictive accuracy.

Results

AdaBoost, SVM, and DT were the best performing MLC with a test set accuracy between 82-88% in distinguishing between ADS and HC eyes. Multiple sclerosis (MS) was distinguished from HC with 92% accuracy. In descending order, fovea thickness, inferotemporal ganglion cell layer (GCL) thickness, low contrast visual acuity, outer inferior macular thickness, temporal peripapillary retinal nerve fiber layer and superior GCL thicknesses were the most important contributors to disease classification.

Conclusions

MLC can be used to combine visual metrics and clinical parameters to distinguish ADS from HC, and to predict MS. In addition to commonly used clinical metrics, we identified other structural and functional metrics that contribute importantly to classification. Among the machine learning algorithms tested, AdaBoost, SVM and DT performed best for this model.

Background

We have limited understanding of the risks and impact of COVID-19 in neuroinflammatory diseases (NID) of the central nervous system, particularly among patients receiving disease modifying therapies (DMTs).

Objectives

To report initial results of a planned multi-center year-long prospective study examining the risk and impact of COVID-19 among persons with NID.

Methods

In April 2020, we deployed online questionnaires to individuals in their home environment to assess the prevalence and potential risk factors of COVID-19 symptoms in persons with and without NID.

Results

Our cohort included 1,115 participants (630 NID, 98% MS; 485 reference) as of April 30, 2020. 202 (18%) participants, residing in areas with high COVID-19 case prevalence, met the April 2020 CDC symptom criteria for suspected COVID-19, but only 4% of all participants received testing given testing shortages. Among all participants, those with suspected COVID-19 were younger, more racially diverse, and reported more depression and liver disease. Persons with NID had the same rate of suspected COVID-19 as the reference group. Early changes in disease management included telemedicine visits in 21% and treatment changes in 9% of persons with NID. After adjusting for potential confounders, increasing neurological disability was associated with a greater likelihood of suspected COVID-19 (OR_adj=1.45, 1.17-1.84).

Conclusions

Our study of real-time, patient-reported experience during the COVID-19 pandemic complements physician-reported MS case registries that capture an excess of severe cases. Overall, persons with NID seem to have a risk of suspected COVID-19 similar to the reference population.

Background

Studies examining the role of the microbiota in multiple sclerosis (MS) often focus on the gut bacteria; few have considered a potential role of gut mycobiota. Methods for evaluating gut mycobiota are lacking and require systematic evaluation of sequencing protocols, reference databases, and bioinformatics pipelines to properly investigate possible gut mycobiome influences on MS.

Objectives

We set out to evaluate the performance of different sequencing conditions and analytical approaches for characterizing the gut mycobiome in a cohort of healthy individuals and cases with monophasic acquired demyelinating syndrome (mono ADS) or pediatric-onset MS.

Methods

We first assessed a mock-community control pool of known, staggered quantities of 19 defined fungal organisms. We then assessed 201 stool samples obtained from our cohort of 52 healthy individuals, 49 individuals with mono ADS, and 46 participants with pediatric-onset MS. The fungal internal transcribed spacer (ITS) 2 region was sequenced using the Illumina® MiSeq platform. Varying concentrations of PhiX Control v3 Library spike-in were tested to address low-complexity amplicon sequencing. Generated sequences were characterized by the UNITE database—a curated collection of eukaryotic ITS sequences—in conjunction with three distinct fungal sequence analysis pipelines: LotuS, mothur, and PIPITS.

Results

Taxa identified in our mock-community differed across sequencing conditions but were similar between technical replicates. LotuS correctly classified 7 taxa at species-level, 7 taxa at genus-level, whereas 5 remained unclassified. Mothur correctly identified 5 species-level taxa, 11 genus-level taxa, whereas 3 remained unclassified. Lastly, PIPITS correctly identified only 3 species-level taxa, 12 genus-level, while 4 remained unclassified. We successfully generated sequence data for 112 of 147 (76%) individuals (70 females; 42 males). The mean age at stool sample collection was 17.3 (SD 5.1) years. Of the tested sequencing conditions, a spike-in of 50% PhiX produced the highest-quality reads.

Conclusions

The LotuS pipeline best identified fungal taxa in our mock-community, with optimal resolution to species level. Sequencing read quality was optimal when 50% PhiX was used for sequencing ITS2 amplicon libraries of stool samples. Establishment of this validated sequencing pipeline, confirmed using a mock-community with known fungal identities, will aid characterization of gut mycobiomes for our cohort of individuals with/without pediatric-onset MS.

Background

Previous studies have described extensive microstructural brain tissue abnormalities in pediatric MS patients. However, available techniques do not distinguish the extent to which such abnormalities are due to axonal loss or demyelination. Further, little is known about microstructural brain tissue changes in MOG-associated disorders (MOGad).

Objectives

To apply a combined analysis of magnetization transfer saturation (MT_sat) and multi-shell diffusion-weighted imaging (DWI) with computation of myelin and axonal volume fractions (MVF and AVF) and imaging g-ratio (the ratio between inner and outer diameter of the myelin sheath); to investigate the specific relationship between these metrics in the corpus callosum (CC) and within brain white matter lesions (WML) of pediatric MS and MOGad.

Methods

We acquired standardized 3T brain MRI in 26 healthy controls (HC) (58% females (F), mean age [years (y) (range)] 15y (9-19)); 16 MS (69% F, 17y (14-18), disease duration (DD) 3y (1-7), time from last relapse (TLR) 2y (0-6)); and 11 MOGad (72% F, 12y (8-18), DD 3y (0-6), TLR 1y (0-3), 8/11 relapsing). WML and CC were segmented according to establishes procedures. DWI processing was performed with FSL and DMIPy; MT_sat, MVF, AVF, and g-ratio were computed using the Jargon data management system. We used general linear models to model average MVF, AVF, and g-ratio in the CC and WML of each group, including the factors age, DD, and the interaction term group*DD. Models including sex were tested, and all exhibited lower AIC.

Results

Relative to HC, MS showed decreased CC MVF (-0.018/y, p=0.0304) and AVF (-0.0069/y; p=0.053) and corresponding increased CC g-ratio (0.0084/y, p=0.059) with increased DD. Relative to HC, MOGad showed decreased CC MVF (-0.017/y, p=0.0304) and AVF (-0.0081/y, p=0.014) with increased DD, without significant CC g-ratio changes. Both MS and MOGad showed decreased average WML MVF compared to HC WM (-0.19, p<10^-8; and -0.2, p<10^-8). MOGad also showed decreased average WML AVF (-0.067, p=0.0048) compared to HC. Average WML g-ratio was higher in MS than MOGad (0.17, p=0.0102), but not significantly different from HC in either group. WML MVF, AVF, and g-ratio did not change significantly with DD in MS or MOGad compared to HC.

Conclusions

Both pediatric MS and MOGad exhibited MRI correlates of axonal loss and demyelination in the CC and WML. Our measures of axonal loss in MOGad reinforces recent work warning of potentially long-term impacts on the brain from non-MS demyelinating pathologies.

Background

Multiple sclerosis (MS) and MOG-associated disorders (MOGad) are characterized by hyperintense white matter (WM) lesions on T₂/FLAIR MRI. Conventional imaging is sensitive but does not inform on the specific pathological substrate. Magnetization transfer saturation provides a good myelin measure, and multishell diffusion is sensitive to the axon + myelin assembly. Together, these can be modelled to estimate myelin volume fraction (MVF), axonal volume fraction (AVF) and imaging g-ratio.

Objectives

To quantify gradients of damage to axons and myelin in lesions and surrouding normal appearing white matter, in pediatric MS and MOGad.

Methods

15 MS [67% females (F), mean (range) age [years (y)]: 17y (14-18), disease duration (DD) 3y (0-6), time from last relapse (TLR) 2y (0-6)] and 7 MOGad [86% F, 13y (8-18), DD 3y (0-6), TLR 1y (0-3), 6/7 relapsing] participants received 3T brain MRI. MVF, AVF and g-ratio were computed according to established procedures. T₂ lesions were segmented according to standardized pipelines and WM masks by multi-atlas segmentation. Euclidean distance transforms labelled voxels in normal-appearing WM with the distance to the nearest lesion voxel, and voxels inside lesions with the distance to the nearest non-lesional WM voxel. Mean MVF, AVF and g-ratio were computed on each isodistant surface. Data were modeled using linear mixed models with distance, diagnosis, and their interaction. Knots were used at 0 and 2mm distance.

Results

MVF decreased towards the center of lesions (MOGad: -0.03/mm; MS: -0.05/mm; p values (ps)<0.002; difference n.s.) as did AVF (MOGad: -0.03/mm; MS: -0.01/mm; ps<0.0002; difference p=0.02); this graded damage extended to 2mm outside lesions. Beyond this, AVF continued to increase (MOGad: 0.001/mm; MS: 0.0003/mm; ps<10^-6; difference p<10^-6). Inside lesions, g-ratio increased towards the center in MS (0.03/mm, p<10^-6) and decreased in MOGad (p=0.15; MOGad-MS difference p<10^-4). G-ratio rose with distance outside lesions (MOGad: 0.001/mm; MS: 0.0004/mm; ps<10^-4; difference p<10^-5). AVF and g-ratio were similar between groups (within 2%) at 20mm from lesions; MVF was higher in MS (14%, p=0.08).

Conclusions

MS and MOGad showed myelin and axonal loss of decreasing severity with distance from lesion center, and this damage extended outside visible lesions. However, MOGad exhibited more severe axonal loss within and near lesions. The corresponding decreasing g-ratio relative to MS may indicate preferential loss of small axons in MS, or relatively better remyelination in MOGad.

Background

The gut microbiota may influence multiple sclerosis (MS) onset. Pediatric MS offers the opportunity to examine pathological processes close to risk acquisition.

Objectives

To examine the gut microbiota from stool samples of persons with pediatric onset MS, or monophasic acquired demyelinating syndromes (ADS) and unaffected controls in a case-control study.

Methods

Persons ≤21 years old with symptom onset <18 years of age with either MS (McDonald criteria) or ADS were eligible, as were unaffected controls with no known neurological or immune-mediated condition (migraine, asthma/allergies were permissible) were enrolled via the Canadian Pediatric Demyelinating Disease Network. Stools were collected between Nov/2015–Mar/2018, shipped on ice, and stored at -80°C. The 16S ribosomal RNA gene (V4 region) was amplified from extracted DNA and sequenced via the Illumina MiSeq platform. Amplicon sequence variants were used to compare the gut microbiota by disease status (MS/ADS/controls). The MS cases were also compared by disease-modifying drug (DMD) status (exposed/naïve). Negative binomial regression was used for genus-level analyses, with rate ratios adjusted (aRR) for age and sex.

Results

Of the 32/41/36 included MS/ADS/control participants, 24/23/21 were girls, averaging age 16.5/13.8/15.1 years at stool sample, respectively. The MS/ADS cases were 14.0/6.9 years at symptom onset. The 3 groups (MS/ADS/controls) were relatively similar for: body mass index (median: 22.8/19.7/19.9), presence of constipation (number of participants with a Bristol Stool Scale score of 1 or 2=8/9/7) and diet (% caloric intake for fat (median)=34/35/34 and for fibre (median)=9/10/11 g/day). Nine MS cases (28%) were DMD naïve. Gut microbiota diversity (alpha and beta) did not differ by disease (MS/ADS/controls), or DMD status (all p>0.1), while taxa-level findings did. For example, relative abundance of the Proteobacteria, Sutterella was depleted for MS cases vs controls and MS vs ADS cases (aRR:0.13;95%CI:0.03–0.59 and 0.21;95%CI:0.05–0.98), but did not differ for the ADS cases vs controls or by DMD status for the MS cases (all p>0.1). Several of the butyrate-producing genera within the Clostridia class (Firmicutes phylum) —Ruminococcaceae UCG−003, Lachnospiraceae UCG−008 and UCG−004—exhibited similar patterns.

Conclusions

Gut microbiota diversity was similar for individuals with pediatric MS relative to either monophasic ADS or unaffected controls. However, at the taxa-level, differences were observed which differentiated the MS cases from the monophasic ADS cases and controls.

Background

Social connectivity is known to impact health and cognition. In adults with multiple sclerosis (MS), close-knit social networks have been associated with worsened physical function (Levin et al, ECTRIMS 2019). To date, no studies have explored social networks in pediatric MS, a disease that occurs during a period of formative learning, social exploration, and personal identity.

Objectives

To analyze social networks in a small cohort of adolescents with MS and examine how these networks relate to academic performance.

Methods

We deployed a structured social network questionnaire to 14 adolescents with MS. We assessed academic performance using either the Woodcock Johnson Test of Academic Achievement (WJ) or performance on a statewide standardized achievement test. We defined academic impairment as a z score ≤1.5 standard deviations on the WJ or a score <65 on any statewide exam. Using graph theoretical statistics, we calculated three structural metrics for each individual’s social network: size, constraint, and effective size. Size is the number of network members, excluding the patient. Constraint is the extent to which network members have connections to each other. Effective size, conceptually the inverse of constraint, is the number of members who occupy structurally unique positions. We explored the association between network size, constraint, and effective size and academic impairment using a student t test.

Results

13 out of 14 subjects (93%) were female with a mean age of 16.4 (±3.25) years. Median EDSS was 1 (range 0-3). Median grade level was 12 (range 7-14). 8 of 14 (57%) subjects were academically impaired. Subjects who were academically impaired had a lower mean network size than those without academic impairment (9.75 vs 17.2, p = 0.028). The group with academic impairment had a trend towards higher network constraint (mean 54.9 vs. 30.4, p = 0.0507). Academic impairment was associated with lower average network effective size (3.94 vs 7.16, p = 0.004).

Conclusions

In this small cohort of adolescents with MS, we found that academic performance was inversely related to social network size and effective size. Taken together, these findings suggest that small, closely-knit social networks are associated with lower scholastic performance. These social network trends in children with MS are in line with physical disability data in adults with MS. Future plans include analyzing a dataset of 60 pediatric MS subjects and comparing to healthy controls. Larger, longitudinal studies are needed to determine the full impact of social networks on academic achievement in youth with MS.