Multiple sclerosis disease activity, a multi-biomarker score of disease activity and response to treatment in multiple sclerosis
Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In th...
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Published in: | Frontiers in immunology Vol. 15; p. 1338585 |
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Abstract | Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In this pilot study, we combined multiple biomarkers previously analyzed in RRMS patients into an MS disease activity (MSDA) score to evaluate their ability to predict relapses and treatment response to glatiramer acetate (GA). Response Gene to Complement 32 (RGC-32), FasL, IL-21, SIRT1, phosphorylated SIRT1 (p-SIRT1), and JNK1 p54 levels were used to generate cut-off values for each biomarker. Any value below the cutoff for RGC-32, FasL SIRT1, or p-SIRT1 or above the cutoff for IL-21 or JNK1 p54 was given a +1 value, indicating relapse or lack of response to GA. Any value above the cutoff value for RGC-32, FasL, SIRT1, p-SIRT1 or below that for IL-21 or JNK1 p54 was given a -1 value, indicating clinical stability or response to GA. An MSDA score above +1 indicated a relapse or lack of response to treatment. An MSDA score below -1 indicated clinical stability or response to treatment. Our results showed that the MSDA scores generated using either four or six biomarkers had a higher sensitivity and specificity and significantly correlated with the expanded disability status scale. Although these results suggest that the MSDA test can be useful for monitoring therapeutic response to biologic agents and assessing clinically challenging situations, the present findings need to be confirmed in larger studies. |
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AbstractList | Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In this pilot study, we combined multiple biomarkers previously analyzed in RRMS patients into an MS disease activity (MSDA) score to evaluate their ability to predict relapses and treatment response to glatiramer acetate (GA). Response Gene to Complement 32 (RGC-32), FasL, IL-21, SIRT1, phosphorylated SIRT1 (p-SIRT1), and JNK1 p54 levels were used to generate cut-off values for each biomarker. Any value below the cutoff for RGC-32, FasL SIRT1, or p-SIRT1 or above the cutoff for IL-21 or JNK1 p54 was given a +1 value, indicating relapse or lack of response to GA. Any value above the cutoff value for RGC-32, FasL, SIRT1, p-SIRT1 or below that for IL-21 or JNK1 p54 was given a -1 value, indicating clinical stability or response to GA. An MSDA score above +1 indicated a relapse or lack of response to treatment. An MSDA score below -1 indicated clinical stability or response to treatment. Our results showed that the MSDA scores generated using either four or six biomarkers had a higher sensitivity and specificity and significantly correlated with the expanded disability status scale. Although these results suggest that the MSDA test can be useful for monitoring therapeutic response to biologic agents and assessing clinically challenging situations, the present findings need to be confirmed in larger studies. Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In this pilot study, we combined multiple biomarkers previously analyzed in RRMS patients into an MS disease activity (MSDA) score to evaluate their ability to predict relapses and treatment response to glatiramer acetate (GA). Response Gene to Complement 32 (RGC-32), FasL, IL-21, SIRT1, phosphorylated SIRT1 (p-SIRT1), and JNK1 p54 levels were used to generate cut-off values for each biomarker. Any value below the cutoff for RGC-32, FasL SIRT1, or p-SIRT1 or above the cutoff for IL-21 or JNK1 p54 was given a +1 value, indicating relapse or lack of response to GA. Any value above the cutoff value for RGC-32, FasL, SIRT1, p-SIRT1 or below that for IL-21 or JNK1 p54 was given a -1 value, indicating clinical stability or response to GA. An MSDA score above +1 indicated a relapse or lack of response to treatment. An MSDA score below -1 indicated clinical stability or response to treatment. Our results showed that the MSDA scores generated using either four or six biomarkers had a higher sensitivity and specificity and significantly correlated with the expanded disability status scale. Although these results suggest that the MSDA test can be useful for monitoring therapeutic response to biologic agents and assessing clinically challenging situations, the present findings need to be confirmed in larger studies.Regular assessment of disease activity in relapsing-remitting multiple sclerosis (RRMS) is required to optimize clinical outcomes. Biomarkers can be a valuable tool for measuring disease activity in multiple sclerosis (MS) if they reflect the pathological processes underlying MS pathogenicity. In this pilot study, we combined multiple biomarkers previously analyzed in RRMS patients into an MS disease activity (MSDA) score to evaluate their ability to predict relapses and treatment response to glatiramer acetate (GA). Response Gene to Complement 32 (RGC-32), FasL, IL-21, SIRT1, phosphorylated SIRT1 (p-SIRT1), and JNK1 p54 levels were used to generate cut-off values for each biomarker. Any value below the cutoff for RGC-32, FasL SIRT1, or p-SIRT1 or above the cutoff for IL-21 or JNK1 p54 was given a +1 value, indicating relapse or lack of response to GA. Any value above the cutoff value for RGC-32, FasL, SIRT1, p-SIRT1 or below that for IL-21 or JNK1 p54 was given a -1 value, indicating clinical stability or response to GA. An MSDA score above +1 indicated a relapse or lack of response to treatment. An MSDA score below -1 indicated clinical stability or response to treatment. Our results showed that the MSDA scores generated using either four or six biomarkers had a higher sensitivity and specificity and significantly correlated with the expanded disability status scale. Although these results suggest that the MSDA test can be useful for monitoring therapeutic response to biologic agents and assessing clinically challenging situations, the present findings need to be confirmed in larger studies. |
Author | Mekala, Armugam P Boodhoo, Dallas Cuevas, Jacob Nguyen, Vinh Rus, Violeta Tatomir, Alexandru Chen, Hegang Anselmo, Freidrich Rus, Horea |
AuthorAffiliation | 4 Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Maryland, School of Medicine , Baltimore, MD , United States 1 Department of Neurology, University of Maryland, School of Medicine , Baltimore, MD , United States 2 Neurology Department, Baltimore Veterans Administration Hospital , Baltimore, MD , United States 3 Department of Epidemiology and Public Health, University of Maryland, School of Medicine , Baltimore, MD , United States |
AuthorAffiliation_xml | – name: 2 Neurology Department, Baltimore Veterans Administration Hospital , Baltimore, MD , United States – name: 1 Department of Neurology, University of Maryland, School of Medicine , Baltimore, MD , United States – name: 3 Department of Epidemiology and Public Health, University of Maryland, School of Medicine , Baltimore, MD , United States – name: 4 Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Maryland, School of Medicine , Baltimore, MD , United States |
Author_xml | – sequence: 1 givenname: Alexandru surname: Tatomir fullname: Tatomir, Alexandru organization: Neurology Department, Baltimore Veterans Administration Hospital, Baltimore, MD, United States – sequence: 2 givenname: Freidrich surname: Anselmo fullname: Anselmo, Freidrich organization: Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 3 givenname: Dallas surname: Boodhoo fullname: Boodhoo, Dallas organization: Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 4 givenname: Hegang surname: Chen fullname: Chen, Hegang organization: Department of Epidemiology and Public Health, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 5 givenname: Armugam P surname: Mekala fullname: Mekala, Armugam P organization: Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 6 givenname: Vinh surname: Nguyen fullname: Nguyen, Vinh organization: Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 7 givenname: Jacob surname: Cuevas fullname: Cuevas, Jacob organization: Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 8 givenname: Violeta surname: Rus fullname: Rus, Violeta organization: Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Maryland, School of Medicine, Baltimore, MD, United States – sequence: 9 givenname: Horea surname: Rus fullname: Rus, Horea organization: Neurology Department, Baltimore Veterans Administration Hospital, Baltimore, MD, United States |
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Cites_doi | 10.1016/j.jneuroim.2014.11.004 10.1186/s12974-023-02811-z 10.1177/1352458521993066 10.1007/s12026-390018-9011-x 10.3389/fimmu.2022.979414 10.1016/j.clim.2019.108297 10.1038/s41392-023-01471-y 10.1097/NRL.0b013e31817acf1a 10.1016/j.jneuroim.2013.09.019 10.1016/j.yexmp.2018.07.008 10.1007/978-1-0716-1282-8 10.1007/s12031-014-0476-3 10.1007/s12026-019-09080-0 10.1016/j.yexmp.2017.01.014 10.1016/j.ibneur.2022.11.001 10.1016/S1474-3984422(14)70305-9 10.1038/s41572-018-0041-4 10.1111/ene.13819 10.1146/annurev-immunol-032713-120227 10.1056/NEJMra1401483 10.1016/j.it.2016.06.001 10.1016/j.amjmed.2020.05.049 10.1016/S1474-4422(21)00063-6 10.1016/j.yexmp.2015.03.011 10.1097/WCO.0000000000000622 10.1016/j.yexmp.2013.12.010 10.3389/fnmol.2022.865529 10.1002/wsbm.1583 10.1212/WNL.33.11.1444 10.3389/fimmu.2023.1226130 10.1186/s12974-019-1674-2 10.1016/S0140-6736(18)30481-1 10.1007/s00415-020-10275-x 10.1146/annurev.neuro.30.051606.094313 10.5603/PJNNS.a2020.0037 10.1016/j.msard.2021.103126 10.3390/ijms23115877 10.1016/j.neuron.2018.01.021 10.1016/j.yexmp.2015.09.007 10.1002/ana.22366 10.1097/WCO.0000000000000818 |
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Copyright | Copyright © 2024 Tatomir, Anselmo, Boodhoo, Chen, Mekala, Nguyen, Cuevas, Rus and Rus. Copyright © 2024 Tatomir, Anselmo, Boodhoo, Chen, Mekala, Nguyen, Cuevas, Rus and Rus 2024 Tatomir, Anselmo, Boodhoo, Chen, Mekala, Nguyen, Cuevas, Rus and Rus |
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Keywords | peripheral blood mononuclear cells biomarkers multiple sclerosis JNK1 RGC-32 glatiramer acetate SIRT1 |
Language | English |
License | Copyright © 2024 Tatomir, Anselmo, Boodhoo, Chen, Mekala, Nguyen, Cuevas, Rus and Rus. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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References | Sun (B21) 2023; 8 Perez (B34) 2022; 13 Kacperska (B37) 2015; 56 Kurtzke (B24) 1983; 33 Tatomir (B25) 2018; 66 Hemmer (B28) 2015; 14 Kruszewski (B15) 2015; 99 Polman (B23) 2011; 69 Tatomir (B27) 2022; 13 Wang (B35) 2022; 15 Aung (B39) 2015; 278 Hewes (B16) 2017; 102 Hauser (B5) 2020; 133 Mey (B11) 2023; 15 Vlaicu (B26) 2019; 67 Reich (B8) 2018; 378 Thompson (B4) 2018; 391 Steinman (B12) 2014; 32 Ciriello (B17) 2018; 105 Perrin Ross (B30) 2013; 19 Absinta (B10) 2020; 33 Baecher-Allan (B9) 2018; 97 Sapko (B32) 2020; 54 Zhang (B38) 2014; 266 Thebault (B14) 2022; 28 Giuliani (B40) 2021; 54 Thrower (B29) 2009; 15 Filippi (B6) 2018; 4 Yang (B31) 2022; 23 Ziemssen (B33) 2019; 16 Tian (B22) 2016; 37 Tegla (B20) 2015; 98 Gonzalez-Martinez (B41) 2023; 20 Dobson (B3) 2019; 26 Tegla (B19) 2014; 96 Bar-Or (B7) 2021; 20 Anselmo (B18) 2020; 210 Zanghi (B36) 2023; 14 Trapp (B2) 2008; 31 Oh (B1) 2018; 31 Arneth (B13) 2021; 268 |
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SubjectTerms | Adult Biomarkers Fas Ligand Protein - metabolism Female Glatiramer Acetate - therapeutic use Humans Immunology Immunosuppressive Agents - therapeutic use Interleukins JNK1 Male Middle Aged Mitogen-Activated Protein Kinase 8 - metabolism multiple sclerosis Multiple Sclerosis - diagnosis Multiple Sclerosis - drug therapy Multiple Sclerosis, Relapsing-Remitting - diagnosis Multiple Sclerosis, Relapsing-Remitting - drug therapy peripheral blood mononuclear cells Pilot Projects RGC-32 Severity of Illness Index SIRT1 Sirtuin 1 - metabolism Treatment Outcome |
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Title | Multiple sclerosis disease activity, a multi-biomarker score of disease activity and response to treatment in multiple sclerosis |
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