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Tomizawa Y, Hagiwara A, Hoshino Y, Nakaya M, Kamagata K, Cossu D, Yokoyama K, Aoki S, Hattori N. The glymphatic system as a potential biomarker and therapeutic target in secondary progressive multiple sclerosis. Mult Scler Relat Disord 2024; 83:105437. [PMID: 38244527 DOI: 10.1016/j.msard.2024.105437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/11/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
BACKGROUND Multiple sclerosis (MS) is a refractory immune-mediated inflammatory disease of the central nervous system, and some cases of the major subtype, relapsing-remitting (RR), transition to secondary progressive (SP). However, the detailed pathogenesis, biomarkers, and effective treatment strategies for secondary progressive multiple sclerosis have not been established. The glymphatic system, which is responsible for waste clearance in the brain, is an intriguing avenue for investigation and is primarily studied through diffusion tensor image analysis along the perivascular space (DTI-ALPS). This study aimed to compare DTI-ALPS indices between patients with RRMS and SPMS to uncover potential differences in their pathologies and evaluate the utility of the glymphatic system as a possible biomarker. METHODS A cohort of 26 patients with MS (13 RRMS and 13 SPMS) who met specific criteria were enrolled in this prospective study. Magnetic resonance imaging (MRI), including diffusion MRI, 3D T1-weighted imaging, and relaxation time quantification, was conducted. The ALPS index, a measure of glymphatic function, was calculated using diffusion-weighted imaging data. Demographic variables, MRI metrics, and ALPS indices were compared between patients with RRMS and those with SPMS. RESULTS The ALPS index was significantly lower in the SPMS group. Patients with SPMS exhibited longer disease duration and higher Expanded Disability Status Scale (EDSS) scores than those with RRMS. Despite these differences, the correlations between the EDSS score, disease duration, and ALPS index were minimal, suggesting that the impact of these clinical variables on ALPS index variations was negligible. DISCUSSION Our study revealed the potential microstructural and functional differences between RRMS and SPMS related to glymphatic system impairment. Although disease severity and duration vary among subtypes, their influence on ALPS index differences appears to be limited. This highlights the stronger association between SP conversion and changes in the ALPS index. These findings align with those of previous research, indicating the involvement of the glymphatic system in the progression of MS. CONCLUSION Although the causality remains uncertain, our study suggests that a reduced ALPS index, reflecting glymphatic system dysfunction, may contribute to MS progression, particularly in SPMS. This suggests the potential of the ALPS index as a diagnostic biomarker for SPMS and underscores the potential of the glymphatic system as a therapeutic target to mitigate MS progression. Future studies with larger cohorts and pathological validation are necessary to confirm these findings. This study provides new insights into the pathogenesis of SPMS and the potential for innovative therapeutic strategies.
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Affiliation(s)
- Yuji Tomizawa
- Department of Neurology, School of Medicine, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo, Tokyo 113-8431, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yasunobu Hoshino
- Department of Neurology, School of Medicine, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo, Tokyo 113-8431, Japan
| | - Moto Nakaya
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Davide Cossu
- Department of Neurology, School of Medicine, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo, Tokyo 113-8431, Japan
| | - Kazumasa Yokoyama
- Department of Neurology, School of Medicine, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo, Tokyo 113-8431, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, School of Medicine, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo, Tokyo 113-8431, Japan
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Hjæresen S, Benedikz E, Sejbaek T, Axelsson M, Novakova L, Zhang M, Lycke J, Illes Z, Fex-Svenningsen Å. High temperature requirement A1 and macrophage migration inhibitory factor in the cerebrospinal fluid; a potential marker of conversion from relapsing-remitting to secondary progressive multiple sclerosis. J Neurol Sci 2024; 457:122888. [PMID: 38278096 DOI: 10.1016/j.jns.2024.122888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 11/30/2023] [Accepted: 01/11/2024] [Indexed: 01/28/2024]
Abstract
BACKGROUND Predictive and prognostic biomarkers for multiple sclerosis (MS) remain a significant gap in MS diagnosis and treatment monitoring. Currently, there are no timely markers to diagnose the transition to secondary progressive MS (SPMS). OBJECTIVE This study aims to evaluate the discriminatory potential of the High temperature requirement serine protease (HTRA1)/Macrophage migration inhibitory factor (MIF) cerebrospinal fluid (CSF) ratio in distinguishing relapsing-remitting (RRMS) patients from SPMS patients. METHODS The MIF and HTRA1 CSF levels were determined using ELISA in healthy controls (n = 23), RRMS patients before (n = 22) and after 1 year of dimethyl fumarate treatment (n = 11), as well as in SPMS patients before (n = 11) and after 2 years of mitoxantrone treatment (n = 7). The ability of the HTRA1/MIF ratio to discriminate the different groups was determined using receiver operating curve (ROC) analyses. RESULTS The ratio was significantly increased in treatment naïve RRMS patients while decreased again in SPMS patients at baseline. Systemic administrated disease modifying treatment (DMT) only significantly affected the ratio in RRMS patients. ROC analysis demonstrated that the ratio could discriminate treatment naïve RRMS patients from SPMS patients with 91% sensitivity and 100% specificity. CONCLUSION The HTRA1/MIF ratio is a strong candidate as a MS biomarker for SPMS conversion.
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Affiliation(s)
- Simone Hjæresen
- University of Southern Denmark, Department of Molecular Medicine, Campusvej 55, 5230 Odense M, Denmark
| | - Eirikur Benedikz
- University of Southern Denmark, Faculty of Health Sciences, Campusvej 55, 5230 Odense M, Denmark
| | - Tobias Sejbaek
- University of Southern Denmark, Department of Regional Health Research, 5000 Odense, Denmark; University of Copenhagen, Department of Neurology, Southwest Jutland University Hospital, 6700 Esbjerg, Denmark
| | - Markus Axelsson
- University of Gothenburg, Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Lenka Novakova
- University of Gothenburg, Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mengliang Zhang
- University of Southern Denmark, Department of Molecular Medicine, Campusvej 55, 5230 Odense M, Denmark; BRIDGE - Brain Research InterDisciplinary Guided Excellence, University of Southern Denmark, Odense, Denmark
| | - Jan Lycke
- University of Gothenburg, Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Zsolt Illes
- University of Southern Denmark, Department of Molecular Medicine, Campusvej 55, 5230 Odense M, Denmark; Odense University Hospital, Department of Neurology, Odense, Denmark; BRIDGE - Brain Research InterDisciplinary Guided Excellence, University of Southern Denmark, Odense, Denmark
| | - Åsa Fex-Svenningsen
- University of Southern Denmark, Department of Molecular Medicine, Campusvej 55, 5230 Odense M, Denmark; BRIDGE - Brain Research InterDisciplinary Guided Excellence, University of Southern Denmark, Odense, Denmark.
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Reeve K, On BI, Havla J, Burns J, Gosteli-Peter MA, Alabsawi A, Alayash Z, Götschi A, Seibold H, Mansmann U, Held U. Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis. Cochrane Database Syst Rev 2023; 9:CD013606. [PMID: 37681561 PMCID: PMC10486189 DOI: 10.1002/14651858.cd013606.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.
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Affiliation(s)
- Kelly Reeve
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | - Begum Irmak On
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Joachim Havla
- lnstitute of Clinical Neuroimmunology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | | | - Albraa Alabsawi
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Zoheir Alayash
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Health Services Research in Dentistry, University of Münster, Muenster, Germany
| | - Andrea Götschi
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | | | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ulrike Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
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Herman S, Arvidsson McShane S, Zjukovskaja C, Khoonsari PE, Svenningsson A, Burman J, Spjuth O, Kultima K. Disease phenotype prediction in multiple sclerosis. iScience 2023; 26:106906. [PMID: 37332601 PMCID: PMC10275960 DOI: 10.1016/j.isci.2023.106906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/09/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023] Open
Abstract
Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.
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Affiliation(s)
- Stephanie Herman
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | - Payam Emami Khoonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Anders Svenningsson
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Joachim Burman
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
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A Scoping Review on Body Fluid Biomarkers for Prognosis and Disease Activity in Patients with Multiple Sclerosis. J Pers Med 2022; 12:jpm12091430. [PMID: 36143216 PMCID: PMC9501898 DOI: 10.3390/jpm12091430] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 11/30/2022] Open
Abstract
Multiple sclerosis (MS) is a complex demyelinating disease of the central nervous system, presenting with different clinical forms, including clinically isolated syndrome (CIS), which is a first clinical episode suggestive of demyelination. Several molecules have been proposed as prognostic biomarkers in MS. We aimed to perform a scoping review of the potential use of prognostic biomarkers in MS clinical practice. We searched MEDLINE up to 25 November 2021 for review articles assessing body fluid biomarkers for prognostic purposes, including any type of biomarkers, cell types and tissues. Original articles were obtained to confirm and detail the data reported by the review authors. We evaluated the reliability of the biomarkers based on the sample size used by various studies. Fifty-two review articles were included. We identified 110 molecules proposed as prognostic biomarkers. Only six studies had an adequate sample size to explore the risk of conversion from CIS to MS. These confirm the role of oligoclonal bands, immunoglobulin free light chain and chitinase CHI3L1 in CSF and of serum vitamin D in the prediction of conversion from CIS to clinically definite MS. Other prognostic markers are not yet explored in adequately powered samples. Serum and CSF levels of neurofilaments represent a promising biomarker.
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The Relation between Induced Electric Field and TMS-Evoked Potentials: A Deep TMS-EEG Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between E-field and the ensuing cortical activation, validate in a clinically relevant manner the results of E-field modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.
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Theodorsdottir A, Larsen PV, Nielsen HH, Illes Z, Ravnborg MH. Multiple sclerosis impairment scale and brain MRI in secondary progressive multiple sclerosis. Acta Neurol Scand 2022; 145:332-347. [PMID: 34799851 DOI: 10.1111/ane.13554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/01/2021] [Accepted: 11/02/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To examine the Multiple Sclerosis Impairment Scale (MSIS) in secondary progressive MS (SPMS) in relation to the Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI) outcomes, and mobility. METHODS In this observational single-center study, 68 secondary progressive multiple sclerosis (SPMS) patients were examined by MSIS, EDSS, functional mobility tests of upper/lower extremities, and multimodal MRI. Participants had EDSS ≥3.5, a decline in daily activities over the last year unrelated to relapses, and/or 6-month confirmed disability progression. RESULTS Mean disease duration was 23.1 ± 8.3 years and mean age 54.4 ± 8.1 years. MSIS, EDSS, and their corresponding motor, cerebellar, and sensory subscores correlated (p < .0001). Motor subscores of MSIS correlated stronger with Timed-25-Foot-Walk (T25FW) than pyramidal functional system score (FSS) (p = .03), but EDSS had a stronger correlation to T25FW than the total MSIS score (p = .01). MSIS cerebellar subscore correlated stronger with 9-Hole Peg Test (9-HPT) than cerebellar FSS (p = .04). The sensory MSIS subscore also showed correlation with 9-HPT in contrast to sensory FSS (p = .006). MSIS subscores had stronger correlations with MRI volumetry measures than FSS scores (lesion volume and putamen, thalamus, corpus callosum volumetry, p = .0001-0.0017). CONCLUSION In patients with SPMS, MSIS correlated with functional motor tests. MSIS showed stronger correlations with atrophy of central nervous system areas, and may be more sensitive to scale cerebellar and sensory function than EDSS.
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Affiliation(s)
- Asta Theodorsdottir
- Department of Neurology Odense University Hospital Odense Denmark
- OPEN Odense Patient Data Explorative Network Odense University Hospital Odense Denmark
| | - Pia Veldt Larsen
- Mental Health Services at the Region of Southern Denmark Odense Denmark
| | - Helle Hvilsted Nielsen
- Department of Neurology Odense University Hospital Odense Denmark
- Department of Neurobiology Research Institute of Molecular Medicine University of Southern Denmark Odense Denmark
- Department of Clinical Research BRIDGE ‐ Brain Research – Inter Disciplinary Guided Excellence University of Southern Denmark Odense Denmark
| | - Zsolt Illes
- Department of Neurology Odense University Hospital Odense Denmark
- Department of Neurobiology Research Institute of Molecular Medicine University of Southern Denmark Odense Denmark
- Department of Clinical Research BRIDGE ‐ Brain Research – Inter Disciplinary Guided Excellence University of Southern Denmark Odense Denmark
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Are Neurophysiological Biomarkers Able to Discriminate Multiple Sclerosis Clinical Subtypes? Biomedicines 2022; 10:biomedicines10020231. [PMID: 35203440 PMCID: PMC8869727 DOI: 10.3390/biomedicines10020231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 11/26/2022] Open
Abstract
Secondary progressive multiple sclerosis (SPMS) subtype is retrospectively diagnosed, and biomarkers of the SPMS are not available. We aimed to identify possible neurophysiological markers exploring grey matter structures that could be used in clinical practice to better identify SPMS. Fifty-five people with MS and 31 healthy controls underwent a transcranial magnetic stimulation protocol to test intracortical interneuron excitability in the primary motor cortex and somatosensory temporal discrimination threshold (STDT) to test sensory function encoded in cortical and deep grey matter nuclei. A logistic regression model was used to identify a combined neurophysiological index associated with the SP subtype. We observed that short intracortical inhibition (SICI) and STDT were the only variables that differentiated the RR from the SP subtype. The logistic regression model provided a formula to compute the probability of a subject being assigned to an SP subtype based on age and combined SICI and STDT values. While only STDT correlated with disability level at baseline evaluation, both SICI and STDT were associated with disability at follow-up. SICI and STDT abnormalities reflect age-dependent grey matter neurodegenerative processes that likely play a role in SPMS pathophysiology and may represent easily accessible neurophysiological biomarkers for the SPMS subtype.
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Circulating miRNAs as Potential Biomarkers Distinguishing Relapsing-Remitting from Secondary Progressive Multiple Sclerosis. A Review. Int J Mol Sci 2021; 22:ijms222111887. [PMID: 34769314 PMCID: PMC8584709 DOI: 10.3390/ijms222111887] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/30/2021] [Accepted: 10/31/2021] [Indexed: 12/18/2022] Open
Abstract
Multiple sclerosis (MS) is a debilitating neurodegenerative, highly heterogeneous disease with a variable course. The most common MS subtype is relapsing–remitting (RR), having interchanging periods of worsening and relative stabilization. After a decade, in most RR patients, it alters into the secondary progressive (SP) phase, the most debilitating one with no clear remissions, leading to progressive disability deterioration. Among the greatest challenges for clinicians is understanding disease progression molecular mechanisms, since RR is mainly characterized by inflammatory processes, while in SP, the neurodegeneration prevails. This is especially important because distinguishing RR from the SP subtype early will enable faster implementation of appropriate treatment. Currently, the MS course is not well-correlated with the biomarkers routinely used in clinical practice. Despite many studies, there are still no reliable indicators correlating with the disease stage and its activity degree. Circulating microRNAs (miRNAs) may be considered valuable molecules for the MS diagnosis and, presumably, helpful in predicting disease subtype. MiRNA expression dysregulation is commonly observed in the MS course. Moreover, knowledge of diverse miRNA panel expression between RRMS and SPMS may allow for deterring disability progression through successful treatment. Therefore, in this review, we address the current state of research on differences in miRNA panel expression between the phases.
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Krajnc N, Bsteh G, Berger T. Clinical and Paraclinical Biomarkers and the Hitches to Assess Conversion to Secondary Progressive Multiple Sclerosis: A Systematic Review. Front Neurol 2021; 12:666868. [PMID: 34512500 PMCID: PMC8427301 DOI: 10.3389/fneur.2021.666868] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 07/06/2021] [Indexed: 12/11/2022] Open
Abstract
Conversion to secondary progressive (SP) course is the decisive factor for long-term prognosis in relapsing multiple sclerosis (MS), generally considered the clinical equivalent of progressive MS-associated neuroaxonal degeneration. Evidence is accumulating that both inflammation and neurodegeneration are present along a continuum of pathologic processes in all phases of MS. While inflammation is the prominent feature in early stages, its quality changes and relative importance to disease course decreases while neurodegenerative processes prevail with ongoing disease. Consequently, anti-inflammatory disease-modifying therapies successfully used in relapsing MS are ineffective in SPMS, whereas specific treatment for the latter is increasingly a focus of MS research. Therefore, the prevention, but also the (anticipatory) diagnosis of SPMS, is of crucial importance. The problem is that currently SPMS diagnosis is exclusively based on retrospectively assessing the increase of overt physical disability usually over the past 6–12 months. This inevitably results in a delay of diagnosis of up to 3 years resulting in periods of uncertainty and, thus, making early therapy adaptation to prevent SPMS conversion impossible. Hence, there is an urgent need for reliable and objective biomarkers to prospectively predict and define SPMS conversion. Here, we review current evidence on clinical parameters, magnetic resonance imaging and optical coherence tomography measures, and serum and cerebrospinal fluid biomarkers in the context of MS-associated neurodegeneration and SPMS conversion. Ultimately, we discuss the necessity of multimodal approaches in order to approach objective definition and prediction of conversion to SPMS.
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Affiliation(s)
- Nik Krajnc
- Department of Neurology, Medical University of Vienna, Vienna, Austria.,Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Gabriel Bsteh
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Thomas Berger
- Department of Neurology, Medical University of Vienna, Vienna, Austria
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Ye F, Wang T, Wu X, Liang J, Li J, Sheng W. N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis. J Transl Med 2021; 19:316. [PMID: 34294105 PMCID: PMC8296732 DOI: 10.1186/s12967-021-02981-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/11/2021] [Indexed: 01/01/2023] Open
Abstract
Background Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02981-5.
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Affiliation(s)
- Fei Ye
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Tianzhu Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoxin Wu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jie Liang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiaoxing Li
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wenli Sheng
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. .,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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12
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Meca-Lallana V, Berenguer-Ruiz L, Carreres-Polo J, Eichau-Madueño S, Ferrer-Lozano J, Forero L, Higueras Y, Téllez Lara N, Vidal-Jordana A, Pérez-Miralles FC. Deciphering Multiple Sclerosis Progression. Front Neurol 2021; 12:608491. [PMID: 33897583 PMCID: PMC8058428 DOI: 10.3389/fneur.2021.608491] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 03/11/2021] [Indexed: 12/12/2022] Open
Abstract
Multiple sclerosis (MS) is primarily an inflammatory and degenerative disease of the central nervous system, triggered by unknown environmental factors in patients with predisposing genetic risk profiles. The prevention of neurological disability is one of the essential goals to be achieved in a patient with MS. However, the pathogenic mechanisms driving the progressive phase of the disease remain unknown. It was described that the pathophysiological mechanisms associated with disease progression are present from disease onset. In daily practice, there is a lack of clinical, radiological, or biological markers that favor an early detection of the disease's progression. Different definitions of disability progression were used in clinical trials. According to the most descriptive, progression was defined as a minimum increase in the Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 from a baseline level of 0, 1.0–5.0, and 5.5, respectively. Nevertheless, the EDSS is not the most sensitive scale to assess progression, and there is no consensus regarding any specific diagnostic criteria for disability progression. This review document discusses the current pathophysiological concepts associated with MS progression, the different measurement strategies, the biomarkers associated with disability progression, and the available pharmacologic therapeutic approaches.
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Affiliation(s)
- Virginia Meca-Lallana
- Multiple Sclerosis Unit, Neurology Department, Fundación de Investigación Biomédica, Hospital Universitario de la Princesa, Madrid, Spain
| | | | - Joan Carreres-Polo
- Neuroradiology Section, Radiology Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Sara Eichau-Madueño
- Multiple Sclerosis CSUR Unit, Neurology Department, Hospital Universitario Virgen Macarena, Seville, Spain
| | - Jaime Ferrer-Lozano
- Department of Pathology, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Lucía Forero
- Neurology Department, Hospital Puerta del Mar, Cádiz, Spain
| | - Yolanda Higueras
- Neurology Department, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Hospital Universitario Gregorio Marañón, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense, Madrid, Spain
| | - Nieves Téllez Lara
- Neurology Department, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | - Angela Vidal-Jordana
- Neurology/Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Francisco Carlos Pérez-Miralles
- Neuroimmunology Unit, Neurology Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain.,Department of Medicine, University of València, Valencia, Spain
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