Tixagevimab/Cilgavimab does not prevent COVID-19 in patients with multiple sclerosis and related disorders on B-cell depleting therapies.

BACKGROUND: Patients with MS and related disorders (pwMSARD) on B-cell depleting treatments have attenuated immune responses to vaccination and were eligible to receive tixagevimab/cilgavimab. OBJECTIVES: Understand incidence and severity of COVID-19 in pwMSARD on B-cell depleting therapies who received tixagevimab/cilgavimab compared to an untreated group. METHODS: We conducted a retrospective medical records review of adult pwMSARD on B-cell depleting treatments who received tixagevimab/cilgavimab between 1/2022-1/2023. PwMSARD on B-cell depleting treatments who did not served as a control group (CG). We compared COVID-19 incidence and severity within 6 months of tixagevimab/cilgavimab or rituximab/ocrelizumab infusion for the CG. RESULTS: 210 patients were identified, 135 in the treatment group (TG) and 75 in the CG. In the TG, 24 (17.8 %) developed COVID-19 compared to 12 (16 %) in the CG. There was no difference in the odds of developing COVID-19 in an unadjusted logistic regression model (OR=1.14; 95 % CI: 0.53, 2.42; p = 0.74) or after adjusting for age and disease duration (OR=1.05; 95 % CI: 0.47, 2.37; p = 0.91). There was also no difference in COVID-19 severity between groups. CONCLUSIONS: There was no difference in COVID-19 infection rates or severity in pwMSARD on B-cell depleting treatments who received tixagevimab/cilgavimab compared to those who remained untreated.

Accuracy of serum neurofilament light to identify contrast-enhancing lesions in multiple sclerosis.

BACKGROUND: Contrast-enhancing magnetic resonance imaging (MRI) lesions (CELs) indicate acute multiple sclerosis inflammation. Serum biomarkers, neurofilament light (sNfL), and glial fibrillary acidic protein (sGFAP) may increase in the presence of CELs, and indicate a need to perform MRI. OBJECTIVE: We assessed the accuracy of biomarkers to detect CELs. METHODS: Patients with two gadolinium-enhanced MRIs and serum biomarkers tested within 3 months were included (N = 557, 66% female). Optimal cut-points from Bland-Altman analysis for spot biomarker level and Youden's index for delta-change from remission were evaluated. RESULTS: A total of 116 patients (21%) had CELs. A spot sNfL measurement >23.0 pg/mL corresponded to 7.0 times higher odds of CEL presence (95% CI: 3.8, 12.8), with 25.9% sensitivity, 95.2% specificity, operating characteristic curve (AUC) 0.61; while sNfL delta-change >30.8% from remission corresponded to 5.0 times higher odds (95% CI: 3.2, 7.8), 52.6% sensitivity, 81.9% specificity, AUC 0.67. sGFAP had poor CEL detection. In patients > 50 years, neither cut-point remained significant. sNfL delta-change outperformed spot levels at identifying asymptomatic CELs (AUC 0.67 vs 0.59) and in patients without treatment escalation between samples (AUC 0.67 vs 0.57). CONCLUSION: Spot sNfL >23.0 pg/mL or a 30.8% increase from remission provides modest prediction of CELs in patients <50 years; however, low sNfL does not obviate the need for MRI.

Multiple sclerosis lesions that impair memory map to a connected memory circuit.

BACKGROUND: Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit. METHODS: We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes. RESULTS: Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit. CONCLUSIONS: Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Early miR-320b and miR-25-3p miRNA levels correlate with multiple sclerosis severity at 10 years: a cohort study.

BACKGROUND: Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder which may cause long-term disability. MicroRNA (miRNA) are stable, non-coding molecules that have been identified in our Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB)-cohort, as well as other international cohorts, as potential disease biomarkers in MS. However, few studies have evaluated the association of miRNA expression early in the MS disease course with long-term outcomes. Therefore, we aimed to evaluate the potential role of three candidate serum miRNAs previously correlated with MS disability in patients with MS, miR-320b, miR-25-3p and miRNA 486-5p, as early biomarkers of MS disability at 10-year follow-up. MAIN BODY: We included 144 patients with serum obtained within three years of MS onset. miRNA expression was measured by RNA extraction followed by RT-PCR. Demographic, clinical, brain MRI and other biomarkers were collected. The primary outcome was the association between early miRNA expression and retaining benign MS, defined as EDSS ≤ 2 at 10-year follow-up. Among the 144 patients, 104 were benign and 40 were not benign at 10-year follow-up. 89 (62%) were women, with mean age at onset 37.7 (SD: 9.6) years. Patients who retained benign MS had lower values of miR-25-3p (p = 0.047) and higher miR-320b (p = 0.025) values. Development of SPMS was associated with higher miR-320b (p = 0.002) levels. Brain parenchymal fraction at year 10 was negatively correlated with miR-25-3p (p = 0.0004) and positively correlated with miR-320b (p = 0.006). No association was found between miR-486-5p and any outcome, and 10-year T2-lesion volume was not associated with any miRNA. CONCLUSIONS: Our results show that miR-320b and miR-25-3p expression are early biomarkers associated with MS severity and brain atrophy. This study provides class III evidence of that miR-320b and miR-25-3p are associated with long-term MS disability which may be a potential tool to risk-stratify patients with MS for early treatment decisions.

miRNA 548a-3p as biomarker of NEDA-3 at 2 years in multiple sclerosis patients treated with fingolimod.

BACKGROUND: Multiple sclerosis (MS) is a disabling autoimmune demyelinating disorder affecting young people and causing significant disability. In the last decade, different microRNA (miRNA) expression patterns have been associated to several treatment response therapies such as interferon and glatiramer acetate. Nowadays, there is increasing interest in the potential role of miRNA as treatment response biomarkers to the most recent oral and intravenous treatments. In this study, we aimed to evaluate serum miRNAs as biomarkers of No Evidence of Disease Activity (NEDA-3) at 2 years in patients with relapsing remitting MS (RRMS) treated with fingolimod. MAIN BODY: A Discovery cohort of 31 RRMS patients treated with fingolimod were identified from the CLIMB study and classified as No Evidence of Disease Activity (NEDA-3) or Evidence of Disease Activity (EDA-3) after 2 years on treatment. Levels of miRNA expression were measured at 6 months using human serum miRNA panels and compared in EDA-3 and NEDA-3 groups using the Wilcoxon rank sum test. A set of differentially expressed miRNA was further validated in an independent cohort of 22 fingolimod-treated patients. We found that 548a-3p serum levels were higher levels in fingolimod-treated patients classified as NEDA-3, compared to the EDA-3 group in both the Discovery (n = 31; p = 0.04) and Validation (n = 22; p = 0.03) cohorts 6 months after treatment initiation; miR-548a-3p provided an AUC of 0.882 discriminating patients with NEDA-3 at 2 years in the Validation cohort. CONCLUSION: Our results show differences in miR-548a-3p expression at 6 months after fingolimod start in patients with MS with NEDA-3 at 2 years. These results provide class III evidence of the use of miR-548a-3p as biomarker of NEDA-3 in patients with fingolimod.

Preserved T cell but attenuated antibody response in MS patients on fingolimod and ocrelizumab following 2nd and 3rd SARS-CoV-2 mRNA vaccine.

Background: There is limited knowledge about T cell responses in patients with multiple sclerosis (MS) after 3 doses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccine. Objectives: Assess the SARS-CoV-2 spike antibody and T cell responses in MS patients and healthy controls (HCs) after 2 doses (2-vax) and 3 doses (3-vax) of SARS-CoV-2 mRNA vaccination. Methods: We studied seroconversion rates and T cell responses by flow cytometry in HC and MS patients on fingolimod or ocrelizumab. Results: After 2-vax, 8/33 (24.2%) patients in ocrelizumab group, 5/7 (71.4%) in fingolimod group, and 29/29 (100%) in HC group (P = 5.7 × 10-11) seroconverted. After 3-vax, 9/22 (40.9%) patients in ocrelizumab group, 19/21 (90.5%) in fingolimod group, and 7/7 (100%) in HC group seroconverted (P = 0.0003). The percentage of SARS-CoV-2 peptide reactive total CD4+ T cells increased in HC and ocrelizumab group but not in fingolimod group after 2-vax and 3-vax (P < 0.0001). The percentage of IFNγ and TNFα producing total CD4+ and CD8+ T cells increased in fingolimod group as compared to HC and ocrelizumab group after 2-vax and 3-vax (P < 0.0001). Conclusions: MS patients on ocrelizumab and fingolimod had attenuated humoral responses, but preserved cytokine producing T cell responses compared to HCs after SARS-CoV-2 mRNA vaccination. Clinical Trials Registration: NCT05060354.

Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes.

BACKGROUND: Early risk-stratification in multiple sclerosis (MS) may impact treatment decisions. Current predictive models have identified that clinical and imaging characteristics of aggressive disease are associated with worse long-term outcomes. Serum biomarkers, neurofilament (sNfL) and glial fibrillary acidic protein (sGFAP), reflect subclinical disease activity through separate pathological processes and may contribute to predictive models of clinical and MRI outcomes. METHODS: We conducted a retrospective analysis of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB study), where patients with multiple sclerosis are seen every 6 months and undergo Expanded Disability Status Scale (EDSS) assessment, have annual brain MRI scans where volumetric analysis is conducted to calculate T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and donate a yearly blood sample for subsequent analysis. We included patients with newly diagnosed relapsing-remitting MS and serum samples obtained at baseline visit and 1-year follow-up (both within 3 years of onset), and were assessed at 10-year follow-up. We measured sNfL and sGFAP by single molecule array at baseline visit and at 1-year follow-up. A predictive clinical model was developed using age, sex, Expanded Disability Status Scale (EDSS), pyramidal signs, relapse rate, and spinal cord lesions at first visit. The main outcome was odds of developing of secondary progressive (SP)MS at year 10. Secondary outcomes included 10-year EDSS, brain T2LV and BPF. We compared the goodness-of-fit of the predictive clinical model with and without sNfL and sGFAP at baseline and 1-year follow-up, for each outcome by area under the receiver operating characteristic curve (AUC) or R-squared. RESULTS: A total 144 patients with median MS onset at age 37.4 years (interquartile range: 29.4-45.4), 64% female, were included. SPMS developed in 25 (17.4%) patients. The AUC for the predictive clinical model without biomarker data was 0.73, which improved to 0.77 when both sNfL and sGFAP were included in the model (P = 0.021). In this model, higher baseline sGFAP associated with developing SPMS (OR=3.3 [95%CI:1.1,10.6], P = 0.04). Adding 1-year follow-up biomarker levels further improved the model fit (AUC = 0.79) but this change was not statistically significant (P = 0.15). Adding baseline biomarker data also improved the R-squared of clinical models for 10-year EDSS from 0.24 to 0.28 (P = 0.032), while additional 1-year follow-up levels did not. Baseline sGFAP was associated with 10-year EDSS (ß=0.58 [95%CI:0.00,1.16], P = 0.05). For MRI outcomes, baseline biomarker levels improved R-squared for T2LV from 0.12 to 0.27 (P<0.001), and BPF from 0.15 to 0.20 (P = 0.042). Adding 1-year follow-up biomarker data further improved T2LV to 0.33 (P = 0.0065) and BPF to 0.23 (P = 0.048). Baseline sNfL was associated with T2LV (ß=0.34 [95%CI:0.21,0.48], P<0.001) and 1-year follow-up sNfL with BPF (ß=-2.53% [95%CI:-4.18,-0.89], P = 0.003). CONCLUSIONS: Early biomarker levels modestly improve predictive models containing clinical and MRI variables. Worse clinical outcomes, SPMS and EDSS, are associated with higher sGFAP levels and worse MRI outcomes, T2LV and BPF, are associated with higher sNfL levels. Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice.

Serum NfL but not GFAP predicts cognitive decline in active progressive multiple sclerosis patients.

BACKGROUND: Cognitive decline is inadequately captured by the standard neurological examination. Serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) are biomarkers of neuronal damage and astrocytic reactivity that may offer an accessible measure of the multiple sclerosis (MS) pathology linked to cognitive decline. OBJECTIVE: To investigate the association of sNfL and sGFAP with cognitive decline in MS patients at high risk for progressive pathology. METHODS: We included 94 MS patients with sustained Expanded Disability Status Score (EDSS) ⩾ 3, available serum samples and cognitive assessment performed by symbol digit modalities test (SDMT) over a median of 3.1 years. The visit for sGFAP/sNfL quantification was at confirmed EDSS ⩾ 3. Linear regression analysis on log-transformed sGFAP/sNfL assessed the association with current and future SDMT. Analyses were adjusted for age, sex, EDSS, treatment group, and recent relapse. RESULTS: sNfL was significantly associated with concurrent SDMT (adjusted change in mean SDMT = -4.5; 95% confidence interval (CI): -8.7, -0.2; p = 0.039) and predicted decline in SDMT (adjusted change in slope: -1.14; 95% CI: -1.83, -0.44; p = 0.001), particularly in active patients. sGFAP was not associated with concurrent or future SDMT. CONCLUSIONS: Higher levels of sNfL were associated with cognitive impairment and predicted cognitive decline in MS patients at high risk for having an underlying progressive pathology.

Serum GFAP and NfL Levels Differentiate Subsequent Progression and Disease Activity in Patients With Progressive Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Neurodegeneration and astrocytic activation are pathologic hallmarks of progressive multiple sclerosis (MS) and can be quantified by serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP). We investigated sNfL and sGFAP as tools for stratifying patients with progressive MS based on progression and disease activity status. METHODS: We leveraged our Comprehensive Longitudinal Investigation of MS at the Brigham and Women's Hospital (CLIMB) natural history study, which includes clinical, MRI data and serum samples collected over more than 20 years. We included patients with MS with a confirmed Expanded Disability Status Scale (EDSS) score ≥3 that corresponds with our classifier for patients at high risk of underlying progressive pathology. We analyzed sNfL and sGFAP within 6 months from the confirmed EDSS score ≥3 corresponding with our baseline visit. Patients who further developed 6-month confirmed disability progression (6mCDP) were classified as progressors. We further stratified our patients into active/nonactive based on new brain/spinal cord lesions or relapses in the 2 years before baseline or during follow-up. Statistical analysis on log-transformed sGFAP/sNfL assessed the baseline association with demographic, clinical, and MRI features and associations with future disability. RESULTS: We included 257 patients with MS who had an average EDSS score of 4.0 and a median follow-up after baseline of 7.6 years. sNfL was higher in patients with disease activity in the 2 years before baseline (adjusted ß = 1.21; 95% CI 1.04-1.42; p = 0.016), during the first 2 years of follow-up (adjusted ß = 1.17; 95% CI = 1.01-1.36; p = 0.042). sGFAP was not increased in the presence of disease activity. Higher sGFAP levels, but not sNfL levels, were associated with higher risk of 6mCDP (adjusted hazard ratio [HR] = 1.71; 95% CI = 1.19-2.45; p = 0.004). The association was stronger in patients with low sNfL (adjusted HR = 2.44; 95% CI 1.32-4.52; p = 0.005) and patients who were nonactive in the 2 years prior or after the sample. DISCUSSION: Higher levels of sGFAP correlated with subsequent progression, particularly in nonactive patients, whereas sNfL reflected acute disease activity in patients with MS at high risk of underlying progressive pathology. Thus, sGFAP and sNfL levels may be used to stratify patients with progressive MS for clinical research studies and clinical trials and may inform clinical care.

Humoral response to COVID-19 vaccination in MS patients on disease modifying therapy: Immune profiles and clinical outcomes.

BACKGROUND: Patients with multiple sclerosis (MS) on some disease modifying therapies (DMTs), particularly anti-CD20 and sphingosine-1-phosphate (S1P) modulators, are at increased risk of severe Coronavirus Disease 19 (COVID-19) and death. COVID-19 vaccinations are effective in preventing infection and severe disease, but humoral response to vaccination and outcomes of COVID-19 infection after vaccination in MS patients on DMTs remain less understood. METHODS: In this retrospective single-center study, patients enrolled in the CLIMB (Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women's Hospital) study and biorepository who had been vaccinated against COVID-19 and had SARS-CoV-2 spike antibody (anti-SARS-CoV-2 S Roche-Elecsys) testing were identified and compared to healthy controls. Demographic data, serum immune profiles including lymphocyte count, B-cell count, and immunoglobulins, and clinical outcome of COVID-19 infection were collected. RESULTS: 254 patients (73.2% female, mean (SD) age 52.9 (11.2) years) were identified. When controlling for age, time since vaccination, and vaccine type, patients on fingolimod, ocrelizumab, rituximab, mycophenolate mofetil, natalizumab and teriflunomide had significantly lower levels of spike antibodies compared to healthy controls (n = 34). Longer duration of treatment was associated with lower spike antibody levels in patients on anti-CD20 therapy (p = 0.016) and S1P modulators (p = 0.016) compared to healthy controls. In patients on anti-CD20 therapy, higher spike antibody levels were associated with higher CD20 cell count (p<0.001), and longer time since last anti-CD20 therapy infusion (p<0.001). 92.8% (13/14) vaccine responders (spike antibody titer >100 ug/dL) on anti-CD20 therapy demonstrated B-cell reconstitution (mean CD20 3.6%). Only 1 out of 86 patients with CD20 of 0% had a measurable spike antibody response to vaccination. During follow-up (mean 270 days), five patients were diagnosed with COVID-19 after vaccination (incidence 1.9%), all of whom had spike antibody < 20 ug/dL. No patients required ICU care or died. CONCLUSIONS: Patients on some DMTs demonstrate reduced humoral immunity after Sars-CoV-2 vaccination. Longer duration of anti-CD20 therapy and reduced CD20 cell count is associated with blunted humoral response to vaccination. CD20 reconstitution >0.1% appears necessary, but not always sufficient, for humoral response to vaccination. Breakthrough COVID-19 infection in our cohort of MS patients on DMT was higher than in population studies. We propose that adjustment of B-cell therapy administration to allow for B-cell reconstitution prior to vaccination should be considered.

Younger age at multiple sclerosis onset is associated with worse outcomes at age 50.

OBJECTIVE: Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50. METHODS: We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50. RESULTS: We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037). CONCLUSIONS: All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor.

Challenges to Longitudinal Characterization of Lower Urinary Tract Dysfunction in Multiple Sclerosis.

BACKGROUND: Neurogenic lower urinary tract dysfunction (LUTD) results in lower urinary tract symptoms (LUTS) that impact quality of life in people with multiple sclerosis (PwMS). The risk factors and the contribution of LUTD to multiple sclerosis (MS) disease progression are under-researched. OBJECTIVE: To identify clinical and demographic predictors of LUTS in PwMS and gaps in clinical ascertainment. METHODS: Participants were adults with MS enrolled in a prospective, multicenter study (SUMMIT, N=802), including a subset of N = 258 patients in the UCSF EPIC study for whom medical records were further reviewed. Demographic (age, sex, race, ethnicity), clinical (disease duration, MS type), and female-specific reproductive factors (e.g., parity) were evaluated to determine associations with bowel/bladder functional system score. Participants' medical records were analyzed to understand the patterns of LUTS ascertainment by physicians and the specific contribution of LUTS to overall bowel/bladder functional system scores. RESULTS: 802 participants (71.3% female) contributed to these analyses. Higher bowel/bladder functional system scores, indicating worsening symptoms and function, were significantly associated with female sex (p=0.001) and progressive MS type (p≤ 0.001). In the EPIC participants, female-specific reproductive exposures (parity, menopause) were not significantly associated with worse bowel/bladder functional system scores. Most (98%) bowel/bladder functional system scores reflected the severity of LUTS (relative to bowel dysfunction). LUTS were under-ascertained clinically, and more so in women (X2 = 5.02, p=0.08). CONCLUSIONS: Female sex and MS type are predictive of worsening LUTS. Symptoms may be less likely to be ascertained by clinicians in females compared to males.

Early Predictors of Clinical and MRI Outcomes Using Least Absolute Shrinkage and Selection Operator (LASSO) in Multiple Sclerosis.

OBJECTIVE: The objective of this study was to identify predictors in common between different clinical and magnetic resonance imaging (MRI) outcomes in multiple sclerosis (MS) by comparing predictive models. METHODS: We analyzed 704 patients from our center seen at MS onset, measuring 37 baseline demographic, clinical, treatment, and MRI predictors, and 10-year outcomes. Our primary aim was identifying predictors in common among clinical outcomes: aggressive MS, benign MS, and secondary-progressive (SP)MS. We also investigated MRI outcomes: T2 lesion volume (T2LV) and brain parenchymal fraction (BPF). The performance of the full 37-predictor model was compared with a least absolute shrinkage and selection operator (LASSO)-selected model of predictors in common between each outcome by the area under the receiver operating characteristic curves (AUCs). RESULTS: The full 37-predictor model was highly predictive of clinical outcomes: in-sample AUC was 0.91 for aggressive MS, 0.81 for benign MS, and 0.81 for SPMS. After variable selection, 10 LASSO-selected predictors were in common between each clinical outcome: age, Expanded Disability Status Scale, pyramidal, cerebellar, sensory and bowel/bladder signs, timed 25-foot walk ≥6 seconds, poor attack recovery, no sensory attacks, and time-to-treatment. This reduced model had comparable cross-validation AUC as the full 37-predictor model: 0.84 versus 0.81 for aggressive MS, 0.75 versus 0.73 for benign MS, and 0.76 versus 0.75 for SPMS, respectively. In contrast, 10-year MRI outcomes were more strongly influenced by initial T2LV and BPF than clinical outcomes. INTERPRETATION: Early prognostication of MS is possible using LASSO modeling to identify a limited set of accessible clinical features. These predictive models can be clinically usable in treatment decision making once implemented into web-based calculators. ANN NEUROL 2022;92:87-96.

Serum NfL levels in the first five years predict 10-year thalamic fraction in patients with MS.

BACKGROUND: Serum neurofilament light chain (sNfL) levels are associated with relapses, MRI lesions, and brain volume in multiple sclerosis (MS). OBJECTIVE: To explore the value of early serum neurofilament light (sNfL) measures in prognosticating 10-year regional brain volumes in MS. METHODS: Patients with MS enrolled in the Comprehensive Longitudinal Investigations in MS at Brigham and Women's Hospital (CLIMB) study within five years of disease onset who had annual blood samples from years 1-10 (n = 91) were studied. sNfL was measured with a single molecule array (SIMOA) assay. We quantified global cortical thickness and normalized deep gray matter (DGM) volumes (fractions of the thalamus, caudate, putamen, and globus pallidus) from high-resolution 3 T MRI at 10 years. Correlations between yearly sNfL levels and 10-year MRI outcomes were assessed using linear regression models. RESULTS: sNfL levels from years 1 and 2 were associated with 10-year thalamus fraction. Early sNfL levels were not associated with 10-year putamen, globus pallidus or caudate fractions. At 10 years, cortical thickness was not associated with early sNfL levels, but was weakly correlated with total DGM fraction. CONCLUSIONS: Early sNfL levels correlate with 10-year thalamic volume, supporting its role as a prognostic biomarker in MS.

Relapse recovery in multiple sclerosis: Effect of treatment and contribution to long-term disability.

BACKGROUND: Although recovery from relapses in MS appears to contribute to disability, it has largely been ignored as a treatment endpoint and disability predictor. OBJECTIVE: To identify demographic and clinical predictors of relapse recovery in the first 3 years and examine its contribution to 10-year disability and MRI outcomes. METHODS: Relapse recovery was retrospectively assessed in 360 patients with MS using the return of the Expanded Disability Status Scale (EDSS), Functional System Scale and neurologic signs to baseline at least 6 months after onset. Univariate and multivariable models were used to associate recovery with demographic and clinical factors and predict 10-year outcomes. RESULTS: Recovery from relapses in the first 3 years was better in patients who were younger, on disease-modifying treatment, with a longer disease duration and without bowel or bladder symptoms. For every incomplete recovery, 10-year EDSS increased by 0.6 and 10-year timed 25-foot walk increased by 0.5 s. These outcomes were also higher with older age and higher baseline BMI. Ten-year MRI brain atrophy was associated only with older age, and MRI lesion volume was only associated with smoking. CONCLUSIONS: Early initiation of disease-modifying treatment in MS was associated with improved relapse recovery, which in turn prevented long-term disability.

Gut Microbiome in Progressive Multiple Sclerosis.

OBJECTIVE: This study was undertaken to investigate the gut microbiome in progressive multiple sclerosis (MS) and how it relates to clinical disease. METHODS: We sequenced the microbiota from healthy controls and relapsing-remitting MS (RRMS) and progressive MS patients and correlated the levels of bacteria with clinical features of disease, including Expanded Disability Status Scale (EDSS), quality of life, and brain magnetic resonance imaging lesions/atrophy. We colonized mice with MS-derived Akkermansia and induced experimental autoimmune encephalomyelitis (EAE). RESULTS: Microbiota ß-diversity differed between MS patients and controls but did not differ between RRMS and progressive MS or differ based on disease-modifying therapies. Disease status had the greatest effect on the microbiome ß-diversity, followed by body mass index, race, and sex. In both progressive MS and RRMS, we found increased Clostridium bolteae, Ruthenibacterium lactatiformans, and Akkermansia and decreased Blautia wexlerae, Dorea formicigenerans, and Erysipelotrichaceae CCMM. Unique to progressive MS, we found elevated Enterobacteriaceae and Clostridium g24 FCEY and decreased Blautia and Agathobaculum. Several Clostridium species were associated with higher EDSS and fatigue scores. Contrary to the view that elevated Akkermansia in MS has a detrimental role, we found that Akkermansia was linked to lower disability, suggesting a beneficial role. Consistent with this, we found that Akkermansia isolated from MS patients ameliorated EAE, which was linked to a reduction in RORγt+ and IL-17-producing γδ T cells. INTERPRETATION: Whereas some microbiota alterations are shared in relapsing and progressive MS, we identified unique bacteria associated with progressive MS and clinical measures of disease. Furthermore, elevated Akkermansia in MS may be a compensatory beneficial response in the MS microbiome. ANN NEUROL 2021;89:1195-1211.

Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study.

The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

Evaluating the Association between Enlarged Perivascular Spaces and Disease Worsening in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Enlarged perivascular spaces (EPVSs) have been associated with relapses and brain atrophy in multiple sclerosis (MS). We investigated the association of EPVS with clinical and MRI features of disease worsening in a well-characterized cohort of relapsing-remitting MS patients prospectively followed for up to 10 years. METHODS: Baseline EPVSs were scored on 1.5T MRI in 30 converters to moderate-severe disability, and 30 nonconverters matched for baseline characteristics. RESULTS: EPVS scores were not significantly different between converters and nonconverters, nor associated with accrual of lesions or brain atrophy. CONCLUSIONS: Our preliminary findings from a relatively small study sample argue against a potential use of EPVS as early indicator of risk for disease worsening in relapsing-remitting MS patients in a clinical setting. Although the small sample size and clinical 1.5T MRI may have limited our ability to detect a significant effect, we provided estimates of the association of EPVS with clinical and MRI indicators of disease worsening in a well-characterized cohort of MS patients.