Dataset Information

No Plasmatic Proteomic Signature at Clinical Disease Onset Associated With 11 Year Clinical, Cognitive and MRI Outcomes in Relapsing-Remitting Multiple Sclerosis Patients.

ABSTRACT: Background: The clinical course of relapsing-remitting multiple sclerosis (RRMS) is highly heterogeneous and prognostic biomarkers at time of diagnosis are lacking. Objective: We investigated the predictive value of the plasma proteome at time of diagnosis in RRMS patients. Methods: The plasma proteome was interrogated using a novel aptamer-based proteomics platform, which allows to measure the levels of a predefined set of 1310 proteins. Results: In 67 clinically and radiologically well characterized RRMS patients, we found no association between the plasma proteome at diagnosis and clinical, cognitive or MRI outcomes after 11 years. Conclusions: Proteomics studies on cerebrospinal fluid may be better suited to identify prognostic biomarkers in early RRMS.

SUBMITTER: Bridel C

PROVIDER: S-EPMC6220078 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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No Plasmatic Proteomic Signature at Clinical Disease Onset Associated With 11 Year Clinical, Cognitive and MRI Outcomes in Relapsing-Remitting Multiple Sclerosis Patients.

Bridel Claire C Eijlers Anand J C AJC van Wieringen Wessel N WN Koel-Simmelink Marleen M Leurs Cyra E CE Schoonheim Menno M MM Killestein Joep J Teunissen Charlotte E CE

Frontiers in molecular neuroscience 20181031

Background: The clinical course of relapsing-remitting multiple sclerosis (RRMS) is highly heterogeneous and prognostic biomarkers at time of diagnosis are lacking. Objective: We investigated the predictive value of the plasma proteome at time of diagnosis in RRMS patients. Methods: The plasma proteome was interrogated using a novel aptamer-based proteomics platform, which allows to measure the levels of a predefined set of 1310 proteins. Results: In 67 clinically and ...[more]

PMID: 30429773

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Project description:Background Relapsing-remitting multiple sclerosis (RRMS) is the most common phenotype of multiple sclerosis (MS), and its active stage is characterized by active T2 lesions with or without gadolinium (Gd) enhancement on magnetic resonance imaging (MRI). Natalizumab is indicated as monotherapy in adults with active RRMS in Japan. The main objective of this study was to investigate the long-term effect of natalizumab on disease progression in Japanese patients with RRMS using MRI data. Methods This retrospective, chart review study was conducted at a single center in Japan. The main study outcome was the yearly proportion of patients with active T2-weighted image lesions detected with or without Gd enhancement on brain MRI (incidence rate) after treatment initiation for up to 5 years. Additional endpoints included annual relapse rate (ARR) and expanded disability status scale (EDSS) score. Results This study included data from 85 patients with RRMS who had received natalizumab for ≥ 1 year; of these, 65 (76.5%) were female and the mean ± standard deviation (SD) age at baseline was 37.5 ± 10.0 years. The incidence rate of active T2 lesions was 52.9% (45/85) in the year prior to natalizumab treatment (Year − 1), which decreased to 2.4% and 1.6% in Year 0.5–1.5 and Year 1.5–2.5, respectively. No active T2 lesions were detected in Year 2.5–5.5 in patients who continued natalizumab treatment. EDSS score was stable, improved, and worsened in 61.8%, 26.3%, and 11.8% of patients, respectively. The median (range) EDSS score was 2.0 (0.0–7.0) at baseline (n = 85) and remained within a similar range (median score between 1.0 and 2.25 during Years 1–5). ARR decreased from 1.12 relapses per year at baseline to 0.12 relapses per year during Year 1 and remained below 0.15 relapses per year up to Year 5. Conclusion The results of this first long-term study evaluating the effect of natalizumab on MRI activity and clinical outcomes in Japanese patients with RRMS suggest that natalizumab markedly reduced disease activity and maintained effectiveness over several years. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-023-03297-1.

Project description:BackgroundThe mechanisms driving primary progressive and relapsing-remitting multiple sclerosis (PPMS/RRMS) phenotypes are unknown. Magnetic resonance imaging (MRI) studies support the involvement of gray matter (GM) in the degeneration, highlighting its damage as an early feature of both phenotypes. However, the role of GM microstructure is unclear, calling for new methods for its decryption.PurposeTo investigate the morphometric and microstructural GM differences between PPMS and RRMS to characterize GM tissue degeneration using MRI.Study typeProspective cross-sectional study.SubjectsForty-five PPMS (26 females) and 45 RRMS (32 females) patients.Field strength/sequence3T scanner. Three-dimensional (3D) fast field echo T1-weighted (T1-w), 3D turbo spin echo (TSE) T2-w, 3D TSE fluid-attenuated inversion recovery, and spin echo-echo planar imaging diffusion MRI (dMRI).AssessmentT1-w and dMRI data were employed for providing information about morphometric and microstructural features, respectively. For dMRI, both diffusion tensor imaging and 3D simple harmonics oscillator based reconstruction and estimation models were used for feature extraction from a predefined set of regions. A support vector machine (SVM) was used to perform patients' classification relying on all these measures.Statistical testsDifferences between MS phenotypes were investigated using the analysis of covariance and statistical tests (P < 0.05 was considered statistically significant).ResultsAll the dMRI indices showed significant microstructural alterations between the considered MS phenotypes, for example, the mode and the median of the return to the plane probability in the hippocampus. Conversely, thalamic volume was the only morphometric feature significantly different between the two MS groups. Ten of the 12 features retained by the selection process as discriminative across the two MS groups regarded the hippocampus. The SVM classifier using these selected features reached an accuracy of 70% and a precision of 69%.Data conclusionWe provided evidence in support of the ability of dMRI to discriminate between PPMS and RRMS, as well as highlight the central role of the hippocampus.Level of evidence2 TECHNICAL EFFICACY STAGE: 3.

Dataset Information

No Plasmatic Proteomic Signature at Clinical Disease Onset Associated With 11 Year Clinical, Cognitive and MRI Outcomes in Relapsing-Remitting Multiple Sclerosis Patients.

Publications

No Plasmatic Proteomic Signature at Clinical Disease Onset Associated With 11 Year Clinical, Cognitive and MRI Outcomes in Relapsing-Remitting Multiple Sclerosis Patients.

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