Serum glial fibrillary acidic protein predicts disease progression in multiple sclerosis.

OBJECTIVE: Glial fibrillary acidic protein (GFAP) is expressed in astrocytes and may be a useful marker of non-active progressive multiple sclerosis (MS). We evaluate serum GFAP (sGFAP) in a large cohort of MS patients to determine if it predicts progression independent of relapse activity (PIRA), future gait aid, and conversion to secondary progressive disease (SPMS). METHODS: Adults with clinically isolated syndrome or any subtype of MS who were listed in the Brigham MS Center Research Database and had at least one sGFAP result were included. All clinic visits following first sample were analyzed for PIRA, future gait aid, and conversion to SPMS. Future cognitive dysfunction and fatigue were evaluated as secondary outcomes. RESULTS: In total, 741 patients were included (average age: 42.3, average disease duration: 3.7 years, median EDSS: 2, and median follow-up duration: 10.0 years). Of 643 patients (86.8%) without progressive disease at baseline, 15.9% developed SPMS. Among all 741, 50.5% had PIRA and 18.6% developed a gait aid requirement. sGFAP level predicted PIRA, future gait aid, and conversion to SPMS in univariable models (p < 0.001, <0.001, and 0.002). sGFAP remained predictive for PIRA and future gait aid in multivariable models in those younger than 50 (p = 0.048, 0.003). Change in sGFAP level over time was not predictive. There was no association between sGFAP and future fatigue or cognitive dysfunction. INTERPRETATION: sGFAP helps to predict PIRA, future gait aid, and conversion to SPMS in a large cohort of MS patients. Our data suggest that baseline levels may be more useful than the change over time.

Stigma in Multiple Sclerosis: A Narrative Review of Current Concepts, Measures, and Findings.

Stigma is an undesired differentness associated with a particular characteristic or condition that distinguishes a person as being outside the norm and cueing stereotypes. Stigma is common in people with multiple sclerosis (MS) and is associated with several disease variables including disease duration, age, age of onset, and disease course. Stigma is also associated with psychological and psychosocial variables such as depression, anxiety, and quality of life. This article reviews our current understanding of stigma in people with MS with a focus on the various stigma types including anticipated, experienced, and internalized stigma, and the lack of consistent definitions across studies. It also describes the 7 instruments that are most commonly used to measure stigma in people with MS, and the limitations of each measure. We conclude that a better understanding of stigma that includes standard definitions of stigma types could lead to more direct intervention strategies aimed at reducing particular stigma concepts and resulting in improved health-related quality of life in people with MS.

Increasing Neurofilament and Glial Fibrillary Acidic Protein After Treatment Discontinuation Predicts Multiple Sclerosis Disease Activity.

BACKGROUND AND OBJECTIVES: Stable patients with multiple sclerosis (MS) may discontinue treatment, but the risk of disease activity is unknown. Serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) are biomarkers of subclinical disease activity and may help risk stratification. In this study, sNfL and sGFAP levels in stable patients were evaluated before and after treatment discontinuation to determine association with disease activity. METHODS: This observational study included patients enrolled in the Comprehensive Longitudinal Investigation in MS at the Brigham and Women's Hospital who discontinued treatment after >2 years disease activity-free. Two serum samples within 2 years, before and after treatment stop, were sent for sNfL and sGFAP measurements by single-molecule array. Biannual neurologic examinations and yearly MRI scans determined disease activity by 3 time-to-event outcomes: 6-month confirmed disability worsening (CDW), clinical attacks, and MRI activity (new T2 or contrast-enhancing lesions). Associations between each outcome and log-transformed sNfL and sGFAP levels pretreatment stop and posttreatment stop and the percent change were estimated using multivariable Cox regression analysis adjusting for age, disability, disease duration, and duration from attack before treatment stop. RESULTS: Seventy-eight patients (92% female) discontinued treatment at a median (interquartile range) age of 48.5 years (39.0-55.7) and disease duration of 12.3 years (7.5-18.8) and were followed up for 6.3 years (4.2-8.5). CDW occurred in 27 patients (35%), new attacks in 19 (24%), and new MRI activity in 26 (33%). Higher posttreatment stop sNfL level was associated with CDW (adjusted hazard ratio (aHR) 2.80, 95% CI 1.36-5.76, p = 0.005) and new MRI activity (aHR 3.09, 95% CI 1.42-6.70, p = 0.004). Patients who had >100% increase in sNfL level from pretreatment stop to posttreatment stop had greater risk of CDW (HR 3.87, 95% CI 1.4-10.7, p = 0.009) and developing new MRI activity (HR 4.02, 95% CI 1.51-10.7, p = 0.005). Patients who had >50% increase in sGFAP level also had greater risk of CDW (HR 5.34, 95% CI 1.4-19.9, p = 0.012) and developing new MRI activity (HR 5.16, 95% CI 1.71-15.6, p = 0.004). DISCUSSION: Stable patients who discontinue treatment may be risk stratified by sNfL and sGFAP levels measured before and after discontinuing treatment. Further studies are needed to validate findings and determine whether resuming treatment in patients with increasing biomarker levels reduces risk of subsequent disease activity.

Association between PROMIS10, SF-36 and NeuroQoL in persons with multiple sclerosis.

BACKGROUND: Patient reported outcome measures (PROs) are considered promising tools for use in clinical settings to measure the impact of disease on physical, mental and social well-being from the patient's perspective. The Patient Reported Outcome Measurement Information System Scale v1.1-Global Health (PROMIS-10) is a measure that is well-suited to clinical practice, but the relationships between this measure and longer PRO measures used in multiple sclerosis (MS) research are unknown. METHODS: Subjects enrolled in SysteMS: A Systems Biology Study of Clinical, Radiological, and Molecular Markers in Subjects with MS at the Brigham and Women's Hospital were eligible to contribute to the study. 349 subjects completed three PRO measures at study entry: PROMIS-10, Medical Outcomes Study Short-Form 36 (SF-36), and Quality of Life in Neurological Disorders (Neuro-QoL™). All questions and global scores from PROMIS-10 were correlated with all domain and summary component scores for SF-36 and all domain scores for Neuro-QoL using Pearson's correlation coefficient. Further, the global scores from PROMIS-10 were correlated with the expanded disability status scale (EDSS) and compared between disease categories (relapsing vs progressive MS). RESULTS: Strong correlations were observed between PROMIS-10 questions and SF-36 domains aimed at measuring the same construct. Further, the PROMIS-10 Global Physical Health score was correlated with the Physical Component Score from the SF-36 (r = 0.798), and the PROMIS Global Mental Health score was correlated with the Mental Component Score from the SF-36 (r = 0.726). Strong correlations between PROMIS-10 questions and two Neuro-QoL domains (fatigue and lower extremity function) were observed, but other Neuro-QoL domains were not strongly correlated with PROMIS-10 questions. PROMIS-10 Global Physical Health had stronger relationship to EDSS and disease category compared to the Global Mental Health. CONCLUSIONS: PROMIS-10 questions and global scores are highly correlated with the corresponding domains of SF-36 in PwMS. Neuro-QoL provides different information regarding HRQOL since different domains are being measured.

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 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.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.