The role of CD56bright NK cells in neurodegenerative disorders.

Emerging evidence suggests a potential role for natural killer (NK) cells in neurodegenerative diseases, such as multiple sclerosis, Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. However, the precise function of NK cells in these diseases remains ambiguous. The existence of two NK cell subsets, CD56bright and CD56dim NK cells, complicates the understanding of the contribution of NK cells in neurodegeneration as their functions within the context of neurodegenerative diseases may differ significantly. CD56bright NK cells are potent cytokine secretors and are considered more immunoregulatory and less terminally differentiated than their mostly cytotoxic CD56dim counterparts. Hence, this review focusses on NK cells, specifically on CD56bright NK cells, and their role in neurodegenerative diseases. Moreover, it explores the mechanisms underlying their ability to enter the central nervous system. By consolidating current knowledge, we aim to provide a comprehensive overview on the role of CD56bright NK cells in neurodegenerative diseases. Elucidating their impact on neurodegeneration may have implications for future therapeutic interventions, potentially ameliorating disease pathogenesis.

Ocrelizumab Concentration Is a Good Predictor of SARS-CoV-2 Vaccination Response in Patients with Multiple Sclerosis.

Ocrelizumab, an anti-CD20 monoclonal antibody, counteracts induction of humoral immune responses after severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccinations in patients with multiple sclerosis (MS). We aimed to assess if serum ocrelizumab concentration measured at the time of vaccination could predict the humoral response after SARS-CoV-2 vaccination. In 52 patients with MS, we found ocrelizumab concentration at the time of vaccination to be a good predictor for SARS-CoV-2 IgG anti-RBD titers after vaccination (comparable to B-cell count). As the course of ocrelizumab concentration may be predicted using pharmacokinetic models, this may be a superior biomarker to guide optimal timing for vaccinations in B-cell depleted patients with MS. ANN NEUROL 2023;93:103-108.

The association between weight during early life and multiple sclerosis onset in a nationwide Dutch birth year cohort.

BACKGROUND: The relationship between being overweight during early life and disease course in multiple sclerosis (MS) is unresolved. We investigated the association between being overweight or obese during early life (childhood and adolescence) and MS case status, age of first symptom onset and onset type in people with MS (pwMS) of the same birth year. METHODS: We enrolled 363 PwMS and 125 healthy controls (HC) from Project Y, a Dutch population-based cross-sectional cohort study including all PwMS born in 1966 and age and sex-matched HC. The associations between weight during childhood and adolescence (non-overweight vs. overweight or obese) and MS, age at symptom onset and onset type (relapsing vs. progressive) were assessed using logistic and linear regressions. In addition, sex-separated associations were explored. RESULTS: Being overweight or obese during childhood (OR = 2.82, 95% CI 1.17-6.80) and adolescence (OR = 2.45, 95% CI 1.13-5.34) was associated with developing MS. Furthermore, being overweight or obese during adolescence was associated with a younger age of onset (ß = -0.11, p = 0.041). Of all 47 patients with a primary progressive (PP) onset type, only one patient (2.1%) was overweight or obese during childhood, whereas 45 patients with a relapsing remitting (RR) onset (14.3%) were overweight or obese during childhood (PP vs. RR p = 0.017; PP vs. HC p = 0.676; RR vs. HC, p = 0.015). However, using logistic regression analysis we did not find evidence of a significant association. CONCLUSION: In a nationwide population-based birth year cohort, being overweight or obese during childhood or adolescence is associated with MS prevalence and an earlier age of onset, but does not seem to associate with the type of onset.

Association of age and inflammatory disease activity in the pivotal natalizumab clinical trials in relapsing-remitting multiple sclerosis.

BACKGROUND: Focal inflammatory disease activity in relapsing-remitting multiple sclerosis (RRMS) diminishes with increasing age. Here we use patient-level data from randomised controlled trials (RCTs) of natalizumab treatment in RRMS to investigate the association of age and inflammatory disease activity. METHODS: We used patient-level data from the AFFIRM (natalizumab vs placebo in relapsing-remitting MS, NCT00027300) and SENTINEL (natalizumab plus interferon beta vs interferon beta in relapsing remitting MS, NCT00030966) RCTs. We determined the proportion of participants developing new T2 lesions, contrast-enhancing lesions (CELs) and relapses over 2 years of follow-up as a function of age, and investigated the association of age with time to first relapse using time-to-event analyses. RESULTS: At baseline, there were no differences between age groups in T2 lesion volume and number of relapses in the year before inclusion. In SENTINEL, older participants had a significantly lower number of CELs. During both trials, the number of new CELs and the proportion of participants developing new CELs were significantly lower in older age groups. The number of new T2 lesions and the proportion of participants with any radiological disease activity during follow-up were also lower in older age groups, especially in the control arms. CONCLUSIONS: Older age is associated with a lower prevalence and degree of focal inflammatory disease activity in treated and untreated RRMS. Our findings inform the design of RCTs, and suggest that patient age should be taken into consideration when deciding on immunomodulatory treatment in RRMS.

Decrease of natalizumab drug levels after switching from intravenous to subcutaneous administration in patients with multiple sclerosis.

BACKGROUND: Natalizumab is effective in the treatment of multiple sclerosis (MS). In 2021, the European Medicines Agency approved the subcutaneous (SC) variant of natalizumab which can be used instead of intravenous administration. However, the course of drug levels varies between administration routes, and the Food and Drug Administration rejected the request for approval of natalizumab SC for reasons that were not disclosed. Our objective was to evaluate the course of natalizumab trough drug levels in patients who switched from natalizumab intravenous to SC on various treatment intervals. METHODS: The NEXT-MS trial (N=382) investigates personalised treatment of natalizumab, in which infusion intervals are prolonged based on individual natalizumab trough drug levels. In 2021, an amendment was approved allowing participants to switch from intravenous to SC administration with frequent measurements of natalizumab drug levels and antidrug antibodies (ADAs). Results were compared with linear mixed model analyses. RESULTS: Until December 2022, 15 participants switched to SC natalizumab. Natalizumab drug levels with SC administration were on average 55% lower compared with intravenous administration (Exp (estimate) 0.45, 95% CI 0.39 to 0.53, p<0.001), leading to very low trough drug levels in three patients on extended treatment intervals. No natalizumab ADAs were detected during intravenous or SC treatment. None of the participants on natalizumab SC showed evidence of MS disease activity. CONCLUSIONS: Natalizumab trough drug levels can decrease after switching from natalizumab intravenous to SC administration. We advise to monitor trough drug levels in patients with low natalizumab drug levels during intravenous treatment, patients with higher body mass index or patients on extended treatment intervals who switch to SC administration of natalizumab.

Spinal cord lesions and brain grey matter atrophy independently predict clinical worsening in definite multiple sclerosis: a 5-year, multicentre study.

OBJECTIVES: To evaluate the combined contribution of brain and cervical cord damage in predicting 5-year clinical worsening in a multicentre cohort of definite multiple sclerosis (MS) patients. METHODS: Baseline 3.0T brain and cervical cord T2-weighted and three-dimensional T1-weighted MRI was acquired in 367 patients with MS (326 relapse-onset and 41 progressive-onset) and 179 healthy controls. Expanded Disability Status Scale (EDSS) score was obtained at baseline and after a median follow-up of 5.1 years (IQR=4.8-5.2). At follow-up, patients were classified as clinically stable/worsened according to EDSS changes. Generalised linear mixed models identified predictors of clinical worsening, evolution to secondary progressive (SP) MS and reaching EDSS=3.0, 4.0 and 6.0 milestones at 5 years. RESULTS: At follow-up, 120/367 (33%) patients with MS worsened clinically; 36/256 (14%) patients with relapsing-remitting evolved to SPMS. Baseline predictors of EDSS worsening were progressive-onset versus relapse-onset MS (standardised beta (ß)=0.97), higher EDSS (ß=0.41), higher cord lesion number (ß=0.41), lower normalised cortical volume (ß=-0.15) and lower cord area (ß=-0.28) (C-index=0.81). Older age (ß=0.86), higher EDSS (ß=1.40) and cord lesion number (ß=0.87) independently predicted SPMS conversion (C-index=0.91). Predictors of reaching EDSS=3.0 after 5 years were higher baseline EDSS (ß=1.49), cord lesion number (ß=1.02) and lower normalised cortical volume (ß=-0.56) (C-index=0.88). Baseline age (ß=0.30), higher EDSS (ß=2.03), higher cord lesion number (ß=0.66) and lower cord area (ß=-0.41) predicted EDSS=4.0 (C-index=0.92). Finally, higher baseline EDSS (ß=1.87) and cord lesion number (ß=0.54) predicted EDSS=6.0 (C-index=0.91). CONCLUSIONS: Spinal cord damage and, to a lesser extent, cortical volume loss helped predicting worse 5-year clinical outcomes in MS.

Ocrelizumab concentration and antidrug antibodies are associated with B-cell count in multiple sclerosis.

BACKGROUND: The majority of patients with multiple sclerosis on ocrelizumab have B-cell depletion after standard interval dosing of 26 weeks. With B-cell-guided dosing patients receive their next dose when B-cell repopulation occurs. Prediction of B-cell repopulation using ocrelizumab concentrations could aid in personalising treatment regimes. The objectives of this study were to evaluate the association between ocrelizumab drug concentration, antidrug antibodies (ADAs) and CD19 B-cell count, and to define a cut-off ocrelizumab concentration for start of B-cell repopulation (defined by ≥10 CD19+ B cells/µL). METHODS: In this investigator-initiated prospective study, blood samples at various time points during ocrelizumab treatment were collected from a biobank. Serum ocrelizumab concentrations and ADAs were measured with two different assays developed for this study. Data were analysed using linear mixed effect models. An receiver operating characteristic (ROC) curve was used to determine a cut-off ocrelizumab concentration for start of B-cell repopulation (defined by ≥10 cells/µL). RESULTS: A total of 452 blood samples from 72 patients were analysed. Ocrelizumab concentrations were detectable up until 53.3 weeks after last infusion and ranged between <0.0025 and 204 µg/mL after 1-67 weeks. Ocrelizumab concentration was negatively associated with B-cell count, with body mass index identified as effect modifier. We found a cut-off value of 0.06 µg/mL for start of B-cell repopulation of ≥10 cells/µL. Ocrelizumab ADAs were detectable in four patients (5.7%) with corresponding low ocrelizumab concentrations and start of B-cell repopulation. CONCLUSIONS: Serum ocrelizumab concentration was strongly associated with B-cell count. Measurement of ocrelizumab drug concentrations and ADAs could play an important role to further personalise treatment and predict the start of B-cell repopulation.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes.

BACKGROUND: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.

Whole Exome Sequencing in Multi-Incident Families Identifies Novel Candidate Genes for Multiple Sclerosis.

Multiple sclerosis (MS) is a degenerative disease of the central nervous system in which auto-immunity-induced demyelination occurs. MS is thought to be caused by a complex interplay of environmental and genetic risk factors. While most genetic studies have focused on identifying common genetic variants for MS through genome-wide association studies, the objective of the present study was to identify rare genetic variants contributing to MS susceptibility. We used whole exome sequencing (WES) followed by co-segregation analyses in nine multi-incident families with two to four affected individuals. WES was performed in 31 family members with and without MS. After applying a suite of selection criteria, co-segregation analyses for a number of rare variants selected from the WES results were performed, adding 24 family members. This approach resulted in 12 exonic rare variants that showed acceptable co-segregation with MS within the nine families, implicating the genes MBP, PLK1, MECP2, MTMR7, TOX3, CPT1A, SORCS1, TRIM66, ITPR3, TTC28, CACNA1F, and PRAM1. Of these, three genes (MBP, MECP2, and CPT1A) have been previously reported as carrying MS-related rare variants. Six additional genes (MTMR7, TOX3, SORCS1, ITPR3, TTC28, and PRAM1) have also been implicated in MS through common genetic variants. The proteins encoded by all twelve genes containing rare variants interact in a molecular framework that points to biological processes involved in (de-/re-)myelination and auto-immunity. Our approach provides clues to possible molecular mechanisms underlying MS that should be studied further in cellular and/or animal models.

Myelin Quantification in White Matter Pathology of Progressive Multiple Sclerosis Post-Mortem Brain Samples: A New Approach for Quantifying Remyelination.

Multiple sclerosis (MS) is a demyelinating and neurodegenerative disease of the central nervous system (CNS). Repair through remyelination can be extensive, but quantification of remyelination remains challenging. To date, no method for standardized digital quantification of remyelination of MS lesions exists. This methodological study aims to present and validate a novel standardized method for myelin quantification in progressive MS brains to study myelin content more precisely. Fifty-five MS lesions in 32 tissue blocks from 14 progressive MS cases and five tissue blocks from 5 non-neurological controls were sampled. MS lesions were selected by macroscopic investigation of WM by standard histopathological methods. Tissue sections were stained for myelin with luxol fast blue (LFB) and histological assessment of de- or remyelination was performed by light microscopy. The myelin quantity was estimated with a novel myelin quantification method (MQM) in ImageJ. Three independent raters applied the MQM and the inter-rater reliability was calculated. We extended the method to diffusely appearing white matter (DAWM) and encephalitis to test potential wider applicability of the method. Inter-rater agreement was excellent (ICC = 0.96) and there was a high reliability with a lower- and upper limit of agreement up to -5.93% to 18.43% variation in myelin quantity. This study builds on the established concepts of histopathological semi-quantitative assessment of myelin and adds a novel, reliable and accurate quantitative measurement tool for the assessment of myelination in human post-mortem samples.

Outcome Reporting Bias in Clinical Trials Researching Disease-Modifying Therapy in Patients With Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Outcome reporting bias occurs when publication of trial results is dependent on clinical significance, thereby threatening the validity of trial results. Research on immunomodulatory drugs in multiple sclerosis has thrived in recent years. We aim to comprehensively examine to what extent outcome reporting bias is present in these trials and the possible underlying factors. METHODS: We identified clinical trials evaluating the efficacy and safety of immunomodulatory drugs in patients with multiple sclerosis (MS) registered in ClinicalTrials.gov after September 2007 and completed before the end of 2018. Information about study design, type of funding, and primary and secondary outcome measures was extracted from the registry. Timing of registration in relation to study initiation and subsequent amendments to the planned outcomes were reviewed. Publications related to these trials were identified in several bibliographic databases using the trial registration number. Registered primary and secondary outcomes were recorded for each trial and compared with outcomes in the publication describing the main outcomes of the trial. RESULTS: A search of ClinicalTrials.gov identified 535 eligible registered clinical trials; of these, 101 had a matching publication. Discrepancies between registered and published primary and secondary outcomes were found in 95% of the trials, including discrepancies between the registered and published primary outcomes in 26 publications. Forty-four percent of the published secondary outcomes were not included in the registry. A similar proportion of registered and nonregistered reported primary efficacy outcomes were positive (favoring the intervention). Nonindustry-funded and open-label trials in MS were more prone to selective primary outcome reporting, although these findings did not reach statistical significance. Only two-thirds of the trials were registered in ClinicalTrials.gov before the trial start date, and 62% of trials made amendments in registered outcomes during or after the trial period. DISCUSSION: Selective outcome reporting is prevalent in trials of disease-modifying drugs in people with MS. We propose methods to diminish the occurrence of this bias in future research.

[Treatment of multiple sclerosis]. / Behandelmogelijkheden bij multiple sclerose.

Multiple sclerosis is a chronic inflammatory disease of the central nervous system, caused by an autoimmune reaction. Treatment options have largely increased over the years. In this article, we present two clinical cases. Patient A has a classic relapsing remitting course of multiple sclerosis with satisfactory effect on second line therapy. Patient B had a stable disease course until a new relapse occurred after the initiation of TNF-alpha blocking therapy because of Crohn's disease. The co-occurrence of multiple auto-immune diseases creates challenges, but also opportunities in choosing the right treatment strategy.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies. OBJECTIVE: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal. METHODS: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance. RESULTS: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008). CONCLUSION: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.

Clinical risk stratification: Development and validation of the DAAE score, a tool for estimating patient risk of transition to secondary progressive multiple sclerosis.

BACKGROUND: Because secondary progressive multiple sclerosis (SPMS) is associated with worse prognosis, early predictive tools are needed. We aimed to use systematic literature review and advanced methods to create and validate a clinical tool for estimating individual patient risk of transition to SPMS over five years. METHODS: Data from the Jacobs Multiple Sclerosis Center (JMSC) and the Multiple Sclerosis Center Amsterdam (MSCA) was collected between 1994 and 2022. Participants were relapsing-remitting adult patients at initial evaluation. We created the tool in four stages: (1) identification of candidate predictors from systematic literature review, (2) ordinal cutoff determination, (3) feature selection, (4) feature weighting. RESULTS: Patients in the development/internal-validation/external-validation datasets respectively (n = 787/n = 522/n = 877) had a median age of 44.1/42.4/36.6 and disease duration of 7.7/6.2/4.4 years. From these, 12.6 %/10.2 %/15.4 % converted to SPMS (median=4.9/5.2/5.0 years). The DAAE Score was named from included predictors: Disease duration, Age at disease onset, Age, EDSS. It ranges from 0 to 12 points, with risk groups of very-low=0-2, low=3-7, medium=8-9, and high≥10. Risk of transition to SPMS increased proportionally across these groups in development (2.7 %/7.4 %/18.8 %/40.2 %), internal-validation (2.9 %/6.8 %/26.8 %/36.5 %), and external-validation (7.5 %/9.6 %/22.4 %/37.5 %). CONCLUSION: The DAAE Score estimates individual patient risk of transition to SPMS consistently across datasets internationally using clinically-accessible data. With further validation, this tool could be used for clinical risk estimation.

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis.

BACKGROUND: Digital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression. OBJECTIVE: To study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS. METHODS: In this prospective study, 92 PwMS were included. Clinical tests [Expanded Disability Status Scale (EDSS), Timed 25 Foot Walk test (T25FW), 9-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT)] and structural MRI scans were performed at baseline (M0) and 12-month follow-up (M12). Active monitoring included the smartphone-based Symbol Digit Modalities Test (sSDMT) and 2 Minute Walk Test (s2MWT), while passive monitoring was based on smartphone keystroke dynamics (KD). Linear regression analyses were used to determine cross-sectional and longitudinal relations between digital and clinical outcomes and brain volumes, with age, disease duration and sex as covariates. RESULTS: In PwMS, both sSDMT and SDMT were associated with thalamic volumes and lesion volumes. KD were related to brain, ventricular, thalamic and lesion volumes. No relations were found between s2MWT and MRI volumes. NHPT scores were associated with lesion volumes only, while EDSS and T25FW were not related to MRI. No longitudinal associations were found for any of the outcome measures between M0 and M12. CONCLUSION: Our results show clear cross-sectional correlations between digital biomarkers and brain volumes in PwMS, which were not all present for conventional clinical outcomes, supporting the potential added value of digital monitoring tools.

Glutamate gene polymorphisms predict brain volumes in multiple sclerosis.

BACKGROUND: Several genetic markers have been associated with multiple sclerosis (MS) susceptibility; however, uncovering the genetic aetiology of the complex phenotypic expression of MS has been more difficult so far. The most common approach in imaging genetics is based on mass-univariate linear modelling (MULM), which faces several limitations. OBJECTIVE: Here we apply a novel multivariate statistical model, sparse reduced-rank regression (sRRR), to identify possible associations of glutamate related single nucleotide polymorphisms (SNPs) and multiple MRI-derived phenotypes in MS. METHODS: Seven phenotypes related to brain and lesion volumes for a total number of 326 relapsing-remitting and secondary-progressive MS patients and a total of 3809 glutamate related and control SNPs were analysed with sRRR, which resulted in a ranking of SNPs in decreasing order of importance ('selection probability'). Lasso regression and MULM were used as comparative statistical techniques to assess consistency of the most important associations over different statistical models. RESULTS: Five SNPs within the NMDA-receptor-2A-subunit (GRIN2A) domain were identified by sRRR in association with normalized brain volume (NBV), normalized grey matter volume and normalized white matter volume (NMWM). The association between GRIN2A and both NBV and NWMV was confirmed in MULM and Lasso analysis. CONCLUSIONS: Using a novel, multivariate regression model confirmed by two other statistical approaches we show associations between GRIN2A SNPs and phenotypic variation in NBV and NWMV in this first exploratory study. Replications in independent datasets are now necessary to validate these findings.

Repair what is lost: Neuroprotection through neural stem cells in progressive MS.

Genchi et al.1 report the first phase 1 trial of neural stem cell transplantation in multiple sclerosis showing a reduction in gray matter atrophy. Results give hope for a new era of induced neuroprotection, especially in progressive multiple sclerosis.

Longitudinal Changes in Cognitive Test Scores in Patients With Relapsing-Remitting Multiple Sclerosis: An Analysis of the DECIDE Dataset.

BACKGROUND AND OBJECTIVES: Cognitive impairment is a common and impactful symptom of relapsing-remitting multiple sclerosis (RRMS). Cognitive outcome measures are often used in cross-sectional studies, but their performance as longitudinal outcome measures in clinical trials is not widely researched. In this study, we used data from a large clinical trial to describe change on the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT) over up to 144 weeks of follow-up. METHODS: We used the data set from DECIDE (clinicaltrials.gov identifier NCT01064401), a large randomized controlled RRMS trial to describe change on the SDMT and PASAT over 144 weeks of follow-up. We compared change on these cognitive outcomes with change on the timed 25-foot walk (T25FW), a well-established physical outcome measure. We investigated several definitions for clinically meaningful change: any change, 4-point change, 8-point change, and 20% change for the SDMT, any change, 4-point change, and 20% change for the PASAT, and 20% change for the T25FW. RESULTS: DECIDE included 1,814 trial participants. SDMT and PASAT scores steadily improved throughout follow-up: the SDMT from a mean 48.2 (SD, 16.1) points at baseline to 52.6 (SD 15.2) at 144 weeks and the PASAT from 47.0 (SD 11.3) at baseline to 50.0 (SD 10.8) at 144 weeks. This improvement in scores is most likely due to a practice effect. Throughout the trial, participants were more likely to experience improvement than worsening of their SDMT and PASAT performance, whereas the number of worsening events on the T25FW steadily increased. Changing the definition of clinically meaningful change for the SDMT and PASAT or using a 6-month confirmation changed the overall number of worsening or improvement events but did not affect the overall behavior of these measures. DISCUSSION: Our findings suggest that the SDMT and PASAT scores do not accurately reflect the steady cognitive decline that people with RRMS experience. Both outcomes show postbaseline increases in scores, which complicates the interpretation of these outcome measures in clinical trials. More research into the size of these changes is needed before recommending a general threshold for clinically meaningful longitudinal change.