Person:

Chitnis, Tanuja

Objectives: To assess the potential of an online platform, PatientsLikeMe.com (PLM), for research in multiple sclerosis (MS). An investigation of the role of body mass index (BMI) on MS disease course was conducted to illustrate the utility of the platform. Methods: First, we compared the demographic characteristics of subjects from PLM and from a regional MS center. Second, we validated PLM’s patient-reported outcome measure (MS Rating Scale, MSRS) against standard physician-rated tools. Finally, we analyzed the relation of BMI to the MSRS measure. Results: Compared with 4,039 MS Center patients, the 10,255 PLM members were younger, more educated, and less often male and white. Disease course was more often relapsing remitting, with younger symptom onset and shorter disease duration. Differences were significant because of large sample sizes but small in absolute terms. MSRS scores for 121 MS Center patients revealed acceptable agreement between patient-derived and physician-derived composite scores (weighted kappa = 0.46). The Walking domain showed the highest weighted kappa (0.73) and correlation (rs = 0.86) between patient and physician scores. Additionally, there were good correlations between the patient-reported MSRS composite and walking scores and physician-derived measures: Expanded Disability Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index (composite rs = 0.81, walking rs = 0.84). Finally, using PLM data, we found a modest correlation between BMI and cross-sectional MSRS (rho = 0.17) and no association between BMI and disease course. Conclusions: The PLM population is comparable to a clinic population, and its patient-reported MSRS is correlated with existing clinical instruments. Thus, this online platform may provide a venue for MS investigations with unique strengths (frequent data collection, large sample sizes). To illustrate its applicability, we assessed the role of BMI in MS disease course but did not find a clinically meaningful role for BMI in this setting.

Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

The gut microbiome plays an important role in immune function and has been implicated in several autoimmune disorders. Here we use 16S rRNA sequencing to investigate the gut microbiome in subjects with multiple sclerosis (MS, n=60) and healthy controls (n=43). Microbiome alterations in MS include increases in Methanobrevibacter and Akkermansia and decreases in Butyricimonas, and correlate with variations in the expression of genes involved in dendritic cell maturation, interferon signalling and NF-kB signalling pathways in circulating T cells and monocytes. Patients on disease-modifying treatment show increased abundances of Prevotella and Sutterella, and decreased Sarcina, compared with untreated patients. MS patients of a second cohort show elevated breath methane compared with controls, consistent with our observation of increased gut Methanobrevibacter in MS in the first cohort. Further study is required to assess whether the observed alterations in the gut microbiome play a role in, or are a consequence of, MS pathogenesis.

The objective of this study was to test a new version of the Magnetic Resonance Disease Severity Scale (MRDSS2), incorporating cerebral gray matter (GM) and spinal cord involvement from 3 T MRI, in modeling the relationship between MRI and physical disability or cognitive status in multiple sclerosis (MS). Fifty-five MS patients and 30 normal controls underwent high-resolution 3 T MRI. The patients had an Expanded Disability Status Scale score of 1.6±1.7 (mean±SD). The cerebral normalized GM fraction (GMF), the T2 lesion volume (T2LV), and the ratio of T1 hypointense LV to T2LV (T1/T2) were derived from brain images. Upper cervical spinal cord area (UCCA) was obtained from spinal cord images. A within-subject d-score (difference of MS from normal control) for each MRI component was calculated, equally weighted, and summed to form MRDSS2. With regard to the relationship between physical disability and MRDSS2 or its individual components, MRI–Expanded Disability Status Scale correlations were significant for MRDSS2 (r=0.33, P=0.013) and UCCA (r=−0.33, P=0.015), but not for GMF (P=0.198), T2LV (P=0.707), and T1/T2 (P=0.240). The inclusion of UCCA appeared to drive this MRI–disability relationship in MRDSS2. With regard to cognition, MRDSS2 showed a larger effect size (P=0.035) than its individual components [GMF (P=0.081), T2LV (P=0. 179), T1/T2 (P=0.043), and UCCA (P=0.818)] in comparing cognitively impaired with cognitively preserved patients (defined by the Minimal Assessment of Cognitive Function in MS). Both cerebral lesions (T1/T2) and atrophy (GMF) appeared to drive this relationship. We describe a new version of the MRDSS, which has been expanded to include cerebral GM and spinal cord involvement. MRDSS2 has concurrent validity with clinical status.

Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.

Multiple sclerosis (MS) commonly affects occupational function. We investigated the link between brain MRI and employment status. Patients with MS (n = 100) completed a Work Productivity and Activity Impairment (WPAI) (general health version) survey measuring employment status, absenteeism, presenteeism, and overall work and daily activity impairment. Patients “working for pay” were considered employed; “temporarily not working but looking for work,” “not working or looking for work due to age,” and “not working or looking for work due to disability” were considered not employed. Brain MRI T1 hypointense (T1LV) and T2 hyperintense (T2LV) lesion volumes were quantified. To assess lesional destructive capability, we calculated each subject’s ratio of T1LV to T2LV (T1/T2). Normalized brain parenchymal volume (BPV) assessed brain atrophy. The mean (SD) age was 45.5 (9.7) years; disease duration was 12.1 (8.1) years; 75 % were women, 76 % were relapsing-remitting, and 76 % were employed. T1LV, T1/T2, Expanded Disability Status Scale (EDSS) scores, and activity impairment were lower and BPV was higher in the employed vs. not employed group (Wilcoxon tests, p < 0.05). Age, disease duration, MS clinical subtype, and T2LV did not differ between groups (p > 0.05). In multivariable logistic regression modeling, adjusting for age, sex, and disease duration, higher T1LV predicted a lower chance of employment (p < 0.05). Pearson correlations showed that EDSS was associated with activity impairment (p < 0.05). Disease duration, age, and MRI measures were not correlated with activity impairment or other WPAI outcomes (p > 0.05). We report a link between brain atrophy and lesions, particularly lesions with destructive potential, to MS employment status.

Objective: To identify circulating microRNAs (miRNAs) linked to disease stage and disability in multiple sclerosis (MS). Methods: Sera from 296 participants including patients with MS, other neurologic diseases (Alzheimer disease and amyotrophic lateral sclerosis), and inflammatory diseases (rheumatoid arthritis and asthma) and healthy controls (HCs) were tested. miRNA profiles were determined using LNA (locked nucleic acid)-based quantitative PCR. Patients with MS were categorized according to disease stage and disability. In the discovery phase, 652 miRNAs were measured in sera from 26 patients with MS and 20 HCs. Following this, significant miRNAs (p < 0.05) from the discovery set were validated using quantitative PCR in 58 patients with MS, 30 HCs, and in 74 samples from other disease controls (Alzheimer disease, amyotrophic lateral sclerosis, asthma, and rheumatoid arthritis). Results: We validated 7 miRNAs that differentiate patients with MS from HCs (p < 0.05 in both the discovery and validation phase); miR-320a upregulation was the most significantly changing serum miRNA in patients with MS. We also identified 2 miRNAs linked to disease progression, with miR-27a-3p being the most significant. Ten miRNAs correlated with the Expanded Disability Status Scale of which miR.199a.5p had the strongest correlation with disability. Of the 15 unique miRNAs we identified in the different group comparisons, 12 have previously been reported to be associated with MS but not in serum. Conclusions: Our findings identify circulating serum miRNAs as potential biomarkers to diagnose and monitor disease status in MS. Classification of evidence: This study provides Class III evidence that circulating serum miRNAs can be used as biomarker for MS.

Background: Fingolimod (FTY720), the first oral treatment for multiple sclerosis (MS), blocks immune cell trafficking and prevents disease relapses by downregulation of sphingosine-1-phosphate receptor. We determined the effect of FTY720 on human T cell activation and effector function. Methods: T cells from MS patients and healthy controls were isolated to measure gene expression profiles in the presence or absence of FTY720 using nanostring and quantitative real-time polymerase chain reaction (qPCR). Cytokine protein expression was measured using luminex assay and flow cytometry analysis. Lentivirus vector carrying short hairpin RNA (shRNA) was used to knock down the expression of specific genes in CD4+ T cells. Chromatin immunoprecipitation was performed to assess T cell factor 1 (TCF-1) binding to promoter regions. Luciferase assays were performed to test the direct regulation of interferon gamma (IFN-γ) and granzyme B (GZMB) by TCF-1. Western blot analysis was used to assess the phosphorylation status of Akt and GSK3β. Results: We showed that FTY720 treatment not only affects T cell trafficking but also T cell activation. Patients treated with FTY720 showed a significant reduction in circulating CD4 T cells. Activation of T cells in presence of FTY720 showed a less inflammatory phenotype with reduced production of IFN-γ and GZMB. This decreased effector phenotype of FTY720-treated T cells was dependent on the upregulation of TCF-1. FTY720-induced TCF-1 downregulated the pathogenic cytokines IFN-γ and GZMB by binding to their promoter/enhancer regions and mediating epigenetic modifications. Furthermore, we observed that TCF-1 expression was lower in T cells from multiple sclerosis patients than in those from healthy individuals, and FTY720 treatment increased TCF-1 expression in multiple sclerosis patients. Conclusions: These results reveal a previously unknown mechanism of the effect of FTY720 on human CD4+ T cell modulation in multiple sclerosis and demonstrate the role of TCF-1 in human T cell activation and effector function. Electronic supplementary material The online version of this article (doi:10.1186/s12974-015-0460-z) contains supplementary material, which is available to authorized users.

Background and Purpose: VBM has been widely used to study GM atrophy in MS. MS lesions lead to segmentation and registration errors that may affect the reliability of VBM results. Improved segmentation and registration have been demonstrated by WM LI before segmentation. DARTEL appears to improve registration versus the USM. Our aim was to compare the performance of VBM-DARTEL versus VBM-USM and the effect of LI in the regional analysis of GM atrophy in MS. Materials and Methods: 3T T1 MR imaging scans were acquired from 26 patients with RRMS and 28 age-matched NC. LI replaced WM lesions with normal-appearing WM intensities before image segmentation. VBM analysis was performed in SPM8 by using DARTEL and USM with and without LI, allowing the comparison of 4 VBM methods (DARTEL + LI, DARTEL − LI, USM + LI, and USM − LI). Accuracy of VBM was assessed by using NMI, CC, and a simulation analysis. Results: Overall, DARTEL + LI yielded the most accurate GM maps among the 4 methods (highest NMI and CC, P < .001). DARTEL + LI showed significant GM loss in the bilateral thalami and caudate nuclei in patients with RRMS versus NC. The other 3 methods overestimated the number of regions of GM loss in RRMS versus NC. LI improved the accuracy of both VBM methods. Simulated data suggested the accuracy of the results provided from patient MR imaging analysis. Conclusions: We introduce a pipeline that shows promise in limiting segmentation and registration errors in VBM analysis in MS.