Skip to main content

Advertisement

SpringerLink
Log in

An Update on Diagnostic Laboratory Biomarkers for Multiple Sclerosis

  • Demyelinating Disorders (L. Wooliscroft and D. Bourdette, Section Editors)
  • Published:
Current Neurology and Neuroscience Reports Aims and scope Submit manuscript

Abstract

Purpose

For many patients, the multiple sclerosis (MS) diagnostic process can be lengthy, costly, and fraught with error. Recent research aims to address the unmet need for an accurate and simple diagnostic process through discovery of novel diagnostic biomarkers. This review summarizes recent studies on MS diagnostic fluid biomarkers, with a focus on blood biomarkers, and includes discussion of technical limitations and practical applicability.

Recent Findings

This line of research is in its early days. Accurate and easily obtainable biomarkers for MS have not yet been identified and validated, but several approaches to uncover them are underway.

Summary

Continue efforts to define laboratory diagnostic biomarkers are likely to play an increasingly important role in defining MS at the earliest stages, leading to better long-term clinical outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Freedman MS, et al. Moving toward earlier treatment of multiple sclerosis: Findings from a decade of clinical trials and implications for clinical practice. Mult Scler Relat Disord. 2014;3(2):147–55.

    Article  PubMed  Google Scholar 

  2. Solomon AJ, Corboy JR. The tension between early diagnosis and misdiagnosis of multiple sclerosis. Nat Rev Neurol. 2017;13(9):567–72.

    Article  PubMed  Google Scholar 

  3. Zalc B. One hundred and fifty years ago Charcot reported multiple sclerosis as a new neurological disease. Brain. 2018;141(12):3482–8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kaisey M, et al. Incidence of multiple sclerosis misdiagnosis in referrals to two academic centers. Mult Scler Relat Disord. 2019;30:51–6.

    Article  PubMed  Google Scholar 

  5. Kelly SB, et al. Multiple sclerosis, from referral to confirmed diagnosis: an audit of clinical practice. Mult Scler J. 2011;17(8):1017–21.

    Article  CAS  Google Scholar 

  6. Thompson AJ, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73.

    Article  PubMed  Google Scholar 

  7. Masjuan J, Alvarez-Cermeno JC, Garcia-Barragan N, Diaz-Sanchez M, Espino M, Sadaba MC, et al. Clinically isolated syndromes: a new oligoclonal band test accurately predicts conversion to MS. Neurology. 2006;66(4):576e8.

    Article  Google Scholar 

  8. Tricco AC, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73.

    Article  PubMed  Google Scholar 

  9. Novakova L, et al. Cerebrospinal fluid biomarkers as a measure of disease activity and treatment efficacy in relapsing-remitting multiple sclerosis. J Neurochem. 2017;141(2):296–304.

    Article  CAS  PubMed  Google Scholar 

  10. Disanto G, et al. Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis. Ann Neurol. 2017;81(6):857–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Edwards KR, et al. Neurofilament light chain as an indicator of exacerbation prior to clinical symptoms in multiple sclerosis. Mult Scler Relat Disord. 2019;31:59–61.

    Article  PubMed  Google Scholar 

  12. Delcoigne B, et al. Blood neurofilament light levels segregate treatment effects in multiple sclerosis. Neurology. 2020;94(11):e1201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kuhle J, et al. Blood neurofilament light chain as a biomarker of MS disease activity and treatment response. Neurology. 2019;92(10):e1007–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Quanterix Granted Breakthrough Device Designation from US FDA for NfL Test for Multiple Sclerosis. 2022: quanterix.com. https://www.quanterix.com/press-releases/quanterix-granted-breakthrough-device-designation-from-us-fda-for-nfl-test-for-multiple-sclerosis/

  15. Teunissen CE, et al. Combination of CSF <em>N</em>-acetylaspartate and neurofilaments in multiple sclerosis. Neurology. 2009;72(15):1322.

    Article  CAS  PubMed  Google Scholar 

  16. Dalla Costa G, et al. Prognostic value of serum neurofilaments in patients with clinically isolated syndromes. Neurology. 2019;92(7):e733.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Olesen MN, et al. Cerebrospinal fluid biomarkers for predicting development of multiple sclerosis in acute optic neuritis: a population-based prospective cohort study. J Neuroinflammation. 2019;16(1):59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bjornevik K, et al. Serum Neurofilament Light Chain Levels in Patients With Presymptomatic Multiple Sclerosis. JAMA Neurol. 2020;77(1):58–64.

    Article  PubMed  Google Scholar 

  19. Bjornevik K, et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Science. 2022;375(6578):296–301.

    Article  CAS  PubMed  Google Scholar 

  20. Schneider R, et al. Plasma Neurofilament light levels are increased in people with Radiologically Isolated Syndrome (P1–1.Virtual). Neurology. 2022;98(18 Supplement):2084.

    Google Scholar 

  21. Matute-Blanch C, et al. Neurofilament light chain and oligoclonal bands are prognostic biomarkers in radiologically isolated syndrome. Brain. 2018;141(4):1085–93.

    Article  PubMed  Google Scholar 

  22. Bridel C, et al. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis. JAMA Neurol. 2019;76(9):1035–48.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Eng LF, et al. An acidic protein isolated from fibrous astrocytes. Brain Res. 1971;28(2):351–4.

    Article  CAS  PubMed  Google Scholar 

  24. Probert F, et al. Determination of CSF GFAP, CCN5, and vWF Levels Enhances the Diagnostic Accuracy of Clinically Defined MS From Non-MS Patients With CSF Oligoclonal Bands. Front Immunol. 2022;12.

  25. Högel H, et al. Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity. Mult Scler J. 2018;26(2):210–9.

    Article  Google Scholar 

  26. Abdelhak A, et al. Serum GFAP as a biomarker for disease severity in multiple sclerosis. Sci Rep. 2018;8(1):14798.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Niiranen M, et al. Serum GFAP and NfL levels in benign relapsing-remitting multiple sclerosis. Mult Scler Relat Disord. 2021;56:103280.

    Article  CAS  PubMed  Google Scholar 

  28. Sun M, et al. A candidate biomarker of glial fibrillary acidic protein in CSF and blood in differentiating multiple sclerosis and its subtypes: A systematic review and meta- analysis. Mult Scler Relat Disord. 2021;51:102870.

    Article  CAS  PubMed  Google Scholar 

  29. Momtazmanesh S, et al. Neuronal and glial CSF biomarkers in multiple sclerosis: a systematic review and meta-analysis. Rev Neurosci. 2021;32(6):573–95.

    Article  CAS  PubMed  Google Scholar 

  30. Abdelhak A, et al. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol. 2022;18(3):158–72.

    Article  CAS  PubMed  Google Scholar 

  31. Bracco F, et al. Free light chains in the CSF in multiple sclerosis. J Neurol. 1987;234(5):303–7.

    Article  CAS  PubMed  Google Scholar 

  32. Kricka LJ, Park JY. Assay Principles in Clinical Pathology. In: McManus LM, Mitchell RN, editors. Pathobiology of Human Disease. San Diego: Academic Press; 2014. p. 3207–21.

    Chapter  Google Scholar 

  33. Leurs CE, et al. Kappa free light chains is a valid tool in the diagnostics of MS: A large multicenter study. Mult Scler J. 2019;26(8):912–23.

    Article  Google Scholar 

  34. Gaetani L, et al. Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis. J Neuroimmunol. 2020;339:577108.

    Article  CAS  PubMed  Google Scholar 

  35. Altinier S, et al. Free light chains in cerebrospinal fluid of multiple sclerosis patients negative for IgG oligoclonal bands. Clin Chim Acta. 2019;496:117–20.

    Article  CAS  PubMed  Google Scholar 

  36. Christiansen M, et al. Cerebrospinal fluid free kappa light chains and kappa index perform equal to oligoclonal bands in the diagnosis of multiple sclerosis. Clin Chem Lab Med. 2018;57(2):210–20.

    Article  PubMed  Google Scholar 

  37. Gurtner KM, et al. CSF free light chain identification of demyelinating disease: comparison with oligoclonal banding and other CSF indexes. Clin Chem Lab Med. 2018;56(7):1071–80.

    Article  CAS  PubMed  Google Scholar 

  38. Presslauer S, et al. Kappa Free Light Chains: Diagnostic and Prognostic Relevance in MS and CIS. PLoS One. 2014;9(2):e89945.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Sanz Diaz CT, et al. Evaluation of Kappa Index as a Tool in the Diagnosis of Multiple Sclerosis: Implementation in Routine Screening Procedure. Front Neurol. 2021;12:676527–676527.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Crespi I, et al. Combined use of Kappa Free Light Chain Index and Isoelectrofocusing of Cerebro-Spinal Fluid in Diagnosing Multiple Sclerosis: Performances and Costs. Clin Lab. 2017;63(3):551–9.

    CAS  PubMed  Google Scholar 

  41. Cavalla P, et al. Kappa free light chains index in the differential diagnosis of Multiple Sclerosis from Neuromyelitis optica spectrum disorders and other immune-mediated central nervous system disorders. J Neuroimmunol. 2020;339:577122.

    Article  CAS  PubMed  Google Scholar 

  42. Konen FF, et al. The Increasing Role of Kappa Free Light Chains in the Diagnosis of Multiple Sclerosis. Cells. 2021;10(11):3056.

  43. Saadeh RS, et al. CSF Kappa Free Light Chains: Cutoff Validation for Diagnosing Multiple Sclerosis. Mayo Clin Proc. 2022;97(4):738–51. Along with NfL, this is the MS biomarker most likely to be clinically relevant to readers now and is available as a commercial test.

    Article  CAS  PubMed  Google Scholar 

  44. Saadeh R, et al. CSF kappa Free Light Chains as a Potential Quantitative Alternative to Oligoclonal Bands in Multiple Sclerosis (S37. 001). 2019, AAN Enterprises.

  45. Konen FF, et al. The Impact of Immunomodulatory Treatment on Kappa Free Light Chains as Biomarker in Neuroinflammation. Cells. 2020;9(4):842.

  46. Lotan I, et al. Saliva immunoglobulin free light chain analysis for monitoring disease activity and response to treatment in multiple sclerosis. Mult Scler Relat Disord. 2020;44:102339.

    Article  PubMed  Google Scholar 

  47. Kouchaki E, et al. Correlation of Serum Levels of IL-33, IL-37, Soluble Form of Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), and Circulatory Frequency of VEGFR2- expressing Cells with Multiple Sclerosis Severity. Iran J Allergy Asthma Immunol. 2017;16(4):329–37.

    PubMed  Google Scholar 

  48. Mado H, et al. Plasma Interleukin-33 level in relapsing-remitting multiple sclerosis. Is it negatively correlated with central nervous system lesions in patients with mild disability? Clin Neurol Neurosurg. 2021;206:106700.

    Article  PubMed  Google Scholar 

  49. Alsahebfosoul F, et al. Interleukin-33 plasma levels in patients with relapsing-remitting multiple sclerosis. Biomol Concepts. 2017;8(1):55–60.

    Article  CAS  PubMed  Google Scholar 

  50. Mishima R, et al. High Plasma Osteopontin Levels in Patients With Inflammatory Bowel Disease. J Clin Gastroenterol. 2007;41(2):167–172.

  51. Clemente N, et al. Osteopontin Bridging Innate and Adaptive Immunity in Autoimmune Diseases. J Immunol Res. 2016;2016:7675437.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Agah E, et al. Osteopontin (OPN) as a CSF and blood biomarker for multiple sclerosis: A systematic review and meta-analysis. PLoS One. 2018;13(1):e0190252.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Golabi M, et al. Identification of Potential Biomarkers in the Peripheral Blood Mononuclear Cells of Relapsing–Remitting Multiple Sclerosis Patients. Inflammation, 2022;45(4):1815–1828.

  54. Rabenstein M, et al. Osteopontin directly modulates cytokine expression of primary microglia and increases their survival. J Neuroimmunol. 2016;299:130–8.

    Article  CAS  PubMed  Google Scholar 

  55. Orsi G, et al. Microstructural and functional brain abnormalities in multiple sclerosis predicted by osteopontin and neurofilament light. Mult Scler Relat Disord. 2021;51:102923.

    Article  CAS  PubMed  Google Scholar 

  56. Kariya Y, et al. Increased cerebrospinal fluid osteopontin levels and its involvement in macrophage infiltration in neuromyelitis optica. BBA clinical. 2015;3:126–34.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Han RK, et al. Increased circulating Th17 cell populations and elevated CSF osteopontin and IL-17 concentrations in patients with Guillain-Barré syndrome. J Clin Immunol. 2014;34(1):94–103.

    Article  CAS  PubMed  Google Scholar 

  58. Olsson B, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 2016;15(7):673–84.

    Article  CAS  PubMed  Google Scholar 

  59. Hall S, et al. Longitudinal Measurements of Cerebrospinal Fluid Biomarkers in Parkinson’s Disease. Mov Disord. 2016;31(6):898–905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Matute-Blanch C, et al. Chitinase 3-like 1 is neurotoxic in primary cultured neurons. Sci Rep. 2020;10(1):7118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Cantó E, et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain. 2015;138(4):918–31.

    Article  PubMed  Google Scholar 

  62. Hinsinger G, et al. Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis. Mult Scler J. 2015;21(10):1251–61.

    Article  CAS  Google Scholar 

  63. Comabella M, et al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain. 2010;133(4):1082–93.

    Article  PubMed  Google Scholar 

  64. Borràs E, et al. Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis. Mol Cell Proteomics. 2016;15(1):318–28.

    Article  PubMed  Google Scholar 

  65. Kušnierová P, et al. Determination of chitinase 3-like 1 in cerebrospinal fluid in multiple sclerosis and other neurological diseases. PLoS One. 2020;15(5):e0233519.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Mañé-Martínez MA, et al. Glial and neuronal markers in cerebrospinal fluid in different types of multiple sclerosis. J Neuroimmunol. 2016;299:112–7.

    Article  PubMed  Google Scholar 

  67. Keene CD, et al. Luminex-based quantification of Alzheimer’s disease neuropathologic change in formalin-fixed post-mortem human brain tissue. Lab Invest. 2019;99(7):1056–67.

    Article  PubMed  Google Scholar 

  68. De Fino C, et al. The predictive value of CSF multiple assay in multiple sclerosis: A single center experience. Mult Scler Relat Disord. 2019;35:176–81.

    Article  PubMed  Google Scholar 

  69. Comabella M, et al. CSF Chitinase 3-Like 2 Is Associated With Long-term Disability Progression in Patients With Progressive Multiple Sclerosis. Neurol Neuroimmunol Neuroinflamm. 2021;8(6):e1082.

  70. Lai CP, Breakefield XO. Role of exosomes/microvesicles in the nervous system and use in emerging therapies. Front Physiol. 2012;3:228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zaborowski MP, et al. Extracellular Vesicles: Composition, Biological Relevance, and Methods of Study. Bioscience. 2015;65(8):783–97.

    Article  PubMed  PubMed Central  Google Scholar 

  72. D'Anca M, et al. Extracellular Vesicles in Multiple Sclerosis: Role in the Pathogenesis and Potential Usefulness as Biomarkers and Therapeutic Tools. Cells. 2021;10(7):1733.

  73. Doeuvre L, et al. Cell-derived microparticles: a new challenge in neuroscience. J Neurochem. 2009;110(2):457–68.

    Article  CAS  PubMed  Google Scholar 

  74. Jia L, et al. Blood neuro-exosomal synaptic proteins predict Alzheimer’s disease atthe asymptomatic stage. Alzheimers Dement. 2021;17(1):49–60.

    Article  CAS  PubMed  Google Scholar 

  75. Turpin D, et al. Role of extracellular vesicles in autoimmune diseases. Autoimmun Rev. 2016;15(2):174–83.

    Article  CAS  PubMed  Google Scholar 

  76. Barreca MM, Aliotta E, Geraci F. Extracellular Vesicles in Multiple Sclerosis as Possible Biomarkers: Dream or Reality? Adv Exp Med Biol. 2017;958:1–9.

    Article  CAS  PubMed  Google Scholar 

  77. Pieragostino D, et al. Enhanced release of acid sphingomyelinase-enriched exosomes generates a lipidomics signature in CSF of Multiple Sclerosis patients. Sci Rep. 2018;8(1):3071.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Moyano AL, et al. Sulfatides in extracellular vesicles isolated from plasma of multiple sclerosis patients. J Neurosci Res. 2016;94(12):1579–87.

    Article  CAS  PubMed  Google Scholar 

  79. Galazka G, et al. Multiple sclerosis: Serum-derived exosomes express myelin proteins. Mult Scler. 2018;24(4):449–58.

    Article  CAS  PubMed  Google Scholar 

  80. Pieragostino D, et al. Proteomics characterization of extracellular vesicles sorted by flow cytometry reveals a disease-specific molecular cross-talk from cerebrospinal fluid and tears in multiple sclerosis. J Proteomics. 2019;204:103403.

    Article  CAS  PubMed  Google Scholar 

  81. Dalla Costa G, et al. CSF extracellular vesicles and risk of disease activity after a first demyelinating event. Mult Scler J. 2021;27(10):1606-1610:1352458520987542.

  82. Verderio C, et al. Myeloid microvesicles are a marker and therapeutic target for neuroinflammation. Ann Neurol. 2012;72(4):610–24.

    Article  CAS  PubMed  Google Scholar 

  83. Laso-García F, et al. Therapeutic potential of extracellular vesicles derived from human mesenchymal stem cells in a model of progressive multiple sclerosis. PLoS One. 2018;13(9):e0202590.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Srivastava R, et al. Potassium Channel KIR4.1 as an Immune Target in Multiple Sclerosis. N Engl J Med. 2012;367(2):115–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Brickshawana A, et al. Investigation of the KIR4.1 potassium channel as a putative antigen in patients with multiple sclerosis: a comparative study. Lancet Neurol. 2014;13(8):795–806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Nerrant E, et al. Lack of confirmation of anti-inward rectifying potassium channel4.1 antibodies as reliable markers of multiple sclerosis. Mult Scler J. 2014;20(13):1699–703.

    Article  CAS  Google Scholar 

  87. Brill L, et al. Increased anti-KIR4.1 antibodies in multiple sclerosis: Could it be a marker of disease relapse? Mult Scler J. 2014;21(5):572–9.

    Article  Google Scholar 

  88. Pröbstel AK, et al. Multiple Sclerosis and Antibodies against KIR4.1. N Engl J Med. 2016;374(15):1496–8.

    Article  PubMed  Google Scholar 

  89. Chastre A, Hafler DA, O’Connor KC. Evaluation of KIR4.1 as an Immune Target in Multiple Sclerosis. N Engl J Med. 2016;374(15):1495–6.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Marnetto F, et al. Detection of potassium channel KIR4.1 antibodies in Multiple Sclerosis patients. J Immunol Methods. 2017;445:53–8.

    Article  CAS  PubMed  Google Scholar 

  91. Navas-Madroñal M, et al. Absence of antibodies against KIR4.1 in multiple sclerosis: A three-technique approach and systematic review. Plos One. 2017;12(4):e0175538.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Marino M, et al. Low reliability of anti-KIR4.1(83–120) peptide auto-antibodies in multiple sclerosis patients. Mult Scler. 2018;24(7):910–8.

    Article  CAS  PubMed  Google Scholar 

  93. Absinta M, et al. Association of Chronic Active Multiple Sclerosis Lesions With Disability In Vivo. JAMA Neurol. 2019;76(12):1474–83.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Maggi P, et al. Paramagnetic Rim Lesions are Specific to Multiple Sclerosis: An International Multicenter 3T MRI Study. Ann Neurol. 2020;88(5):1034–42.

    Article  PubMed  Google Scholar 

  95. Suthiphosuwan S, et al. Paramagnetic Rim Sign in Radiologically Isolated Syndrome. JAMA Neurol. 2020;77(5):653–5.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Bergsland N, et al. Serum iron concentration is associated with subcortical deep gray matter iron levels in multiple sclerosis patients. NeuroReport. 2017;28(11):645–8.

    Article  CAS  PubMed  Google Scholar 

  97. Al-Radaideh A, El-Haj N, Hijjawi N. Iron deposition and atrophy in cerebralgrey matter and their possible association with serum iron in relapsing-remitting multiple sclerosis. Clin Imaging. 2021;69:238–42.

    Article  PubMed  Google Scholar 

  98. Magliozzi R, et al. Iron homeostasis, complement, and coagulation cascade as CSF signature of cortical lesions in early multiple sclerosis. Ann Clin Transl Neurol. 2019;6(11):2150–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Starčević Čizmarević N, et al. Lack of association between C282Y and H63D polymorphisms in the hemochromatosis gene and risk of multiple sclerosis: A meta- analysis. Biomed Rep. 2022;16(2):12–12.

    Article  PubMed  Google Scholar 

  100. Trentini A, et al. Evaluation of total, ceruloplasmin-associated and type IIferroxidase activities in serum and cerebrospinal fluid of multiple sclerosis patients. J Neurol Sci. 2017;377:133–6.

    Article  CAS  PubMed  Google Scholar 

  101. Nirooei E, et al. Blood Trace Element Status in Multiple Sclerosis: a Systematic Review and Meta-analysis. Biol Trace Elem Res. 2022;200(1):13–26.

    Article  CAS  PubMed  Google Scholar 

  102. Absinta M, et al. A lymphocyte–microglia–astrocyte axis in chronic active multiple sclerosis. Nature. 2021;597(7878):709–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Munger KL, et al. Anti-Epstein-Barr virus antibodies as serological markers of multiple sclerosis: a prospective study among United States military personnel. Mult Scler. 2011;17(10):1185–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kieff EaR, A.B., Fields Virology. Lippincott Williams & Wilkins; 2001. p. 2511–2573. Philadelphia, PA, USA

  105. Gieß RM, et al. Epstein-Barr virus antibodies in serum and DNA load in saliva are not associated with radiological or clinical disease activity in patients with early multiple sclerosis. PLoS One. 2017;12(4):e0175279.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Sisay S, et al. Untreated relapsing remitting multiple sclerosis patients show antibody production against latent Epstein Barr Virus (EBV) antigens mainly in the periphery and innate immune IL-8 responses preferentially in the CNS. J Neuroimmunol. 2017;306:40–5.

    Article  CAS  PubMed  Google Scholar 

  107. Kuchroo VK, Weiner HL. How does Epstein-Barr virus trigger MS? Immunity. 2022;55(3):390–2.

    Article  CAS  PubMed  Google Scholar 

  108. Ha GE, et al. The Ca2+-activated chloride channel anoctamin-2 mediates spike- frequency adaptation and regulates sensory transmission in thalamocortical neurons. Nat Commun. 2016;7(1):13791.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Ayoglu B, et al. Anoctamin 2 identified as an autoimmune target in multiple sclerosis. Proc Natl Acad Sci. 2016;113(8):2188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Tengvall K, et al. Molecular mimicry between Anoctamin 2 and Epstein-Barr virus nuclear antigen 1 associates with multiple sclerosis risk. Proc Natl Acad Sci U S A. 2019;116(34):16955–60. This paper both addresses a test with a lot of current interest (EBNA) and shows that it may be a specific marker for MS, distinguishing it from clinically relevant mimics, when combined with another marker (ANO2).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Lampert P, Carpenter S. ELECTRON MICROSCOPIC STUDIES ON THE VASCULAR PERMEABILITY AND THE MECHANISM OF DEMYELINATION IN EXPERIMENTAL ALLERGIC ENCEPHALOMYELITIS. J Neuropathol Exp Neurol. 1965;24:11–24.

    Article  CAS  PubMed  Google Scholar 

  112. Gay D, Esiri M. Blood-brain barrier damage in acute multiple sclerosis plaques. An immunocytological study. Brain. 1991;114(Pt 1B):557–72.

    Article  PubMed  Google Scholar 

  113. Lucchinetti CF, et al. Distinct Patterns of Multiple Sclerosis Pathology Indicates Heterogeneity in Pathogenesis. Brain Pathol. 1996;6(3):259–74.

    Article  CAS  PubMed  Google Scholar 

  114. Sati P, et al. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol. 2016;12(12):714–22.

    Article  PubMed  Google Scholar 

  115. Ontaneda D, et al. Central vein sign: A diagnostic biomarker in multiple sclerosis (CAVS- MS) study protocol for a prospective multicenter trial. NeuroImage Clin. 2021;32:102834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Kalinowska-Łyszczarz A, et al. Serum sPECAM-1 and sVCAM-1 levels are associated with conversion to multiple sclerosis in patients with optic neuritis. J Neuroimmunol. 2016;300:11–4.

    Article  PubMed  Google Scholar 

  117. Jasiak-Zatońska M, et al. Different blood-brain-barrier disruption profiles in multiple sclerosis, neuromyelitis optica spectrum disorders, and neuropsychiatric systemic lupus erythematosus. Neurol Neurochir Pol. 2022;56(3):246–255.

  118. Rocha NP, et al. Exploring the relationship between Endothelin-1 andperipheral inflammation in multiple sclerosis. J Neuroimmunol. 2019;326:45–8.

    Article  CAS  PubMed  Google Scholar 

  119. Monti L, Arrigucci U, Rossi A. Insights into Endothelin-3 and Multiple Sclerosis. Biomol Concepts. 2020;11(1):137–41.

    Article  CAS  PubMed  Google Scholar 

  120. Yun JW, et al. Neurolymphatic biomarkers of brain endothelial inflammatory activation: Implications for multiple sclerosis diagnosis. Life Sci. 2019;229:116–23.

    Article  CAS  PubMed  Google Scholar 

  121. Zinger A, et al. Plasma levels of endothelial and B-cell-derived microparticles are restored by fingolimod treatment in multiple sclerosis patients. Mult Scler J. 2016;22(14):1883–7.

    Article  CAS  Google Scholar 

  122. Olsson A, et al. Circulating levels of tight junction proteins in multiple sclerosis: Association with inflammation and disease activity before and after disease modifying therapy. Mult Scler Relat Disord. 2021;54:103136.

    Article  CAS  PubMed  Google Scholar 

  123. Akil E, et al. Serum endocan levels in multiple sclerosis relapse and remission. Eur Rev Med Pharmacol Sci. 2021;25(11):4091–8.

    CAS  PubMed  Google Scholar 

  124. Cirac A, et al. The Aryl Hydrocarbon Receptor-Dependent TGF-α/VEGF-B Ratio Correlates With Disease Subtype and Prognosis in Multiple Sclerosis. Neurol Neuroimmunol Neuroinflamm. 2021;8(5):e1043.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Mazzucco M, et al. CNS endothelial derived extracellular vesicles are biomarkers of active disease in multiple sclerosis. Fluids Barriers CNS. 2022;19(1):13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Wu R, et al. MicroRNA-448 promotes multiple sclerosis development throughinduction of Th17 response through targeting protein tyrosine phosphatase non-receptor type 2 (PTPN2). Biochem Biophys Res Commun. 2017;486(3):759–66.

    Article  CAS  PubMed  Google Scholar 

  127. Paraboschi EM, et al. Genetic association and altered gene expression of mir-155 in multiple sclerosis patients. Int J Mol Sci. 2011;12(12):8695–712.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Zhang J, et al. MicroRNA-155 modulates Th1 and Th17 cell differentiation and is associated with multiple sclerosis and experimental autoimmune encephalomyelitis. J Neuroimmunol. 2014;266(1–2):56–63.

    Article  CAS  PubMed  Google Scholar 

  129. Ahmed Ali M, et al. Relationship between miR-155 and miR-146a polymorphisms and susceptibility to multiple sclerosis in an Egyptian cohort. Biomed Rep. 2020;12(5):276–84.

    PubMed  PubMed Central  Google Scholar 

  130. Mameli G, et al. Natalizumab Therapy Modulates miR-155, miR-26a and Proinflammatory Cytokine Expression in MS Patients. PLoS One. 2016;11(6):e0157153.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Arruda LC, et al. Autologous hematopoietic SCT normalizes miR-16, -155 and -142-3p expression in multiple sclerosis patients. Bone Marrow Transplant. 2015;50(3):380–9.

    Article  CAS  PubMed  Google Scholar 

  132. Michell-Robinson MA, et al. Effects of fumarates on circulating and CNS myeloid cells in multiple sclerosis. Ann Clin Transl Neurol. 2016;3(1):27–41.

    Article  CAS  PubMed  Google Scholar 

  133. Saridas F, et al. The expression and prognostic value of miR-146a and miR-155 in Turkish patients with multiple sclerosis. Neurol Res. 2021;44(3):1–7.

  134. Regev K, et al. Comprehensive evaluation of serum microRNAs as biomarkers in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2016;3(5):e267.

    Article  PubMed  PubMed Central  Google Scholar 

  135. Lücking CB, et al. Association between Early-Onset Parkinson’s Disease and Mutations in the Parkin Gene. N Engl J Med. 2000;342(21):1560–7.

    Article  PubMed  Google Scholar 

  136. Joodi Khanghah O, et al. Evaluation of the Diagnostic and Predictive Value of Serum Levels of ANT1, ATG5, and Parkin in Multiple Sclerosis. Clin Neurol Neurosurg. 2020;197: 106197.

    Article  PubMed  Google Scholar 

  137. Hassanpour M, et al. The relationship between ANT1 and NFL with autophagy and mitophagy markers in patients with multiple sclerosis. J Clin Neurosci. 2020;78:307–12.

    Article  CAS  PubMed  Google Scholar 

  138. Cossu D, et al. Potential of PINK1 and PARKIN Proteins as Biomarkers for Active Multiple Sclerosis: A Japanese Cohort Study. Front Immunol. 2021;12: 681386. This study highlights a novel group of biomarkers not commonly considered related to MS and how they may distinguish MS from common mimics including other neuroimmune diseases.

    Article  PubMed  PubMed Central  Google Scholar 

  139. Tahani S, et al. Elevated serum level of IL-4 in neuromyelitis optica and multiple sclerosis patients. J Immunoassay Immunochem. 2019;40(5):555–63.

    Article  CAS  PubMed  Google Scholar 

  140. Levraut M, et al. Kappa Free Light Chains, Soluble Interleukin-2 Receptor, and Interleukin-6 Help Explore Patients Presenting With Brain White Matter Hyperintensities. Front Immunol. 2022;13:1–12.

  141. Matejčíková Z, et al. Cerebrospinal fluid and serum levels of interleukin-8 in patients with multiple sclerosis and its correlation with Q-albumin. Mult Scler Relat Disord. 2017;14:12–5.

    Article  PubMed  Google Scholar 

  142. Sedeeq MS, et al. Micro-RNA-96 and interleukin-10 are independent biomarkers for multiple sclerosis activity. J Neurol Sci. 2019;403:92–6.

    Article  CAS  PubMed  Google Scholar 

  143. Olesen MN, et al. Cerebrospinal fluid biomarkers for predicting development of multiple sclerosis in acute optic neuritis: a population-based prospective cohort study. J Neuroinflammation. 2019;16(1):59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Li M, et al. Identification and Clinical Validation of Key Extracellular Proteins as the Potential Biomarkers in Relapsing-Remitting Multiple Sclerosis. Front Immunol. 2021;12.

  145. Naderi S, et al. IL-27 plasma level in relapsing remitting multiple sclerosis subjects: The double-faced cytokine. J Immunoassay Immunochem. 2016;37(6):659–70.

    Article  CAS  PubMed  Google Scholar 

  146. Eslami M, et al. Serum levels and genetic variation of IL-35 are associated with multiple sclerosis: a population-based case-control study. Immunol Res. 2022;70(1):75–85.

    Article  CAS  PubMed  Google Scholar 

  147. Alsahebfosoul F, et al. Serum level of interleukin 36 in patients with multiple sclerosis. J Immunoassay Immunochem. 2018;39(5):558–64.

    Article  CAS  PubMed  Google Scholar 

  148. Zarrabi M, et al. Elevated IL-38 Serum Levels in Newly Diagnosed Multiple Sclerosis and Systemic Sclerosis Patients. Med Princ Pract. 2021;30(2):146–53.

    Article  PubMed  Google Scholar 

  149. Badihian S, et al. Decreased serum levels of interleukin-35 among multiple sclerosis patients may be related to disease progression. J Biol Regul Homeost Agents. 2018;32(5):1249–53.

    CAS  PubMed  Google Scholar 

  150. Kurtzke JF. Rating neurologic impairment in multiple sclerosis. Neurology. 1983;33(11):1444.

    Article  CAS  PubMed  Google Scholar 

  151. Bhargava P, et al. Altered Levels of Toll-Like Receptors in Circulating Extracellular Vesicles in Multiple Sclerosis. Cells. 2019;8(9):1058.

  152. Welton JL, et al. Cerebrospinal fluid extracellular vesicle enrichment for protein biomarker discovery in neurological disease; multiple sclerosis. J Extracell Vesicles. 2017;6(1):1369805.

    Article  PubMed  PubMed Central  Google Scholar 

  153. Geraci F, et al. Differences in Intercellular Communication During Clinical Relapse and Gadolinium-Enhanced MRI in Patients With Relapsing Remitting Multiple Sclerosis: A Study of the Composition of Extracellular Vesicles in Cerebrospinal Fluid. Front Cell Neurosci. 2018;12:418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Bhargava P, et al. Synaptic and complement markers in extracellular vesicles in multiple sclerosis. Mult Scler. 2021;27(4):509–18.

    Article  CAS  PubMed  Google Scholar 

  155. Qu X, et al. MiR-30a inhibits Th17 differentiation and demyelination of EAE mice by targeting the IL-21R. Brain Behav Immun. 2016;57:193–9.

    Article  CAS  PubMed  Google Scholar 

  156. Mahmoudian E, et al. Thioredoxin-1, redox factor-1 and thioredoxin-interacting protein, mRNAs are differentially expressed in Multiple Sclerosis patients exposed and non-exposed to interferon and immunosuppressive treatments. Gene. 2017;634:29–36.

    Article  CAS  PubMed  Google Scholar 

  157. Bruinsma IB, et al. Regulator of oligodendrocyte maturation, miR-219, apotential biomarker for MS. J Neuroinflammation. 2017;14(1):235.

    Article  PubMed  PubMed Central  Google Scholar 

  158. Ehya F, et al. Identification of miR-24 and miR-137 as novel candidate multiple sclerosis miRNA biomarkers using multi-staged data analysis protocol. Mol Biol Res Commun. 2017;6(3):127–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Pahlevan Kakhki M, et al. HOTAIR but not ANRIL long non-coding RNA contributes to the pathogenesis of multiple sclerosis. Immunology. 2018;153(4):479–87.

    Article  CAS  PubMed  Google Scholar 

  160. Gharesouran J, et al. A Novel Regulatory Function of Long Non-coding RNAs at Different Levels of Gene Expression in Multiple Sclerosis. J Mol Neurosci. 2019;67(3):434–40.

    Article  CAS  PubMed  Google Scholar 

  161. Gharesouran J, et al. Integrative analysis of OIP5-AS1/HUR1 to discover new potential biomarkers and therapeutic targets in multiple sclerosis. J Cell Physiol. 2019;234(10):17351–60.

    Article  CAS  PubMed  Google Scholar 

  162. Azimi M, et al. Altered Expression of miR-326 in T Cell-derived Exosomes of Patients with Relapsing-remitting Multiple Sclerosis. Iran J Allergy Asthma Immunol. 2019;18(1):108–13.

    PubMed  Google Scholar 

  163. Talebian S, et al. Assessment of expression of RELN signaling pathway in multiple sclerosis patients. Immunobiology. 2019;224(3):402–7.

    Article  CAS  PubMed  Google Scholar 

  164. Salek Esfahani B, et al. Down-regulation of ERMN expression in relapsing remitting multiple sclerosis. Metab Brain Dis. 2019;34(5):1261–6.

    Article  PubMed  Google Scholar 

  165. Elkhodiry AA, Zamzam DA, El Tayebi HM. miR-155 and functional proteins of CD8+ T cells as potential prognostic biomarkers for relapsing-remitting multiple sclerosis. Mult Scler Relat Disord. 2021;53:103078.

    Article  CAS  PubMed  Google Scholar 

  166. Shaker OG, et al. LncRNAs, MALAT1 and lnc-DC as potential biomarkers for multiple sclerosis diagnosis. Biosci Rep. 2019;39(1):BSR20181335.

  167. Mohamed DAW, et al. Apoptotic protease activating factor-1 gene and MicroRNA- 484: A possible interplay in relapsing remitting multiple sclerosis. Mult Scler Relat Disord. 2022;58:103502.

    Article  CAS  PubMed  Google Scholar 

  168. Vistbakka J, et al. Circulating microRNAs as biomarkers in progressive multiple sclerosis. Mult Scler. 2017;23(3):403–12.

    Article  CAS  PubMed  Google Scholar 

  169. Vistbakka J, et al. Evaluation of serum miR-191-5p, miR-24-3p, miR-128-3p, and miR- 376c–3 in multiple sclerosis patients. Acta Neurol Scand. 2018;138(2):130–6.

    Article  CAS  PubMed  Google Scholar 

  170. Ahmadian-Elmi M, et al. miR-27a and miR-214 exert opposite regulatory roles in Th17 differentiation via mediating different signaling pathways in peripheral blood CD4+ T lymphocytes of patients with relapsing-remitting multiple sclerosis. Immunogenetics. 2016;68(1):43–54.

    Article  CAS  PubMed  Google Scholar 

  171. Guan H, et al. Inverse correlation of expression of microRNA-140-5p with progression of multiple sclerosis and differentiation of encephalitogenic T helper type 1 cells. Immunology. 2016;147(4):488–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Golabi M, et al. Identification of Potential Biomarkers in the Peripheral Blood Mononuclear Cells of Relapsing-Remitting Multiple Sclerosis Patients. Inflammation. 2022:1815–1828.

  173. Rahimirad S, et al. Identification of hsa-miR-106a-5p as an impact agent on promotion of multiple sclerosis using multi-step data analysis. Neurol Sci. 2021;42(9):3791–9.

    Article  PubMed  Google Scholar 

  174. Majd M, et al. MiR-9-5p and miR-106a-5p dysregulated in CD4. Iran J Basic Med Sci. 2018;21(3):277–83.

    PubMed  PubMed Central  Google Scholar 

  175. Quintana E, et al. miRNAs in cerebrospinal fluid identify patients with MS and specifically those with lipid-specific oligoclonal IgM bands. Mult Scler. 2017;23(13):1716–26.

    Article  CAS  PubMed  Google Scholar 

  176. Wan C, et al. MicroRNA 182 promotes T helper 1 cell by repressing hypoxia induced factor 1 alpha in experimental autoimmune encephalomyelitis. Eur J Immunol. 2019;49(12):2184–94.

    Article  CAS  PubMed  Google Scholar 

  177. Li L, et al. MiR-1-3p facilitates Th17 differentiation associating with multiple sclerosis via targeting ETS1. Eur Rev Med Pharmacol Sci. 2020;24(12):6881–92.

    CAS  PubMed  Google Scholar 

Download references

Funding

This work was supported in part by National Multiple Sclerosis Society (RFA-2104–37511), Race to Erase MS, and Cedars-Sinai Precision Health Group grants to Marwa Kaisey MD.

Author information

Authors and Affiliations

  1. Cedars-Sinai Medical Center Department of Neurology, 127 S. San Vicente Blvd, A6600, Los Angeles, CA, 90048, USA

    Marwa Kaisey, Ghazal Lashgari, Justyna Fert-Bober & Nancy L. Sicotte

  2. Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave. U10 Mellen Center, Cleveland, OH, 44106, USA

    Daniel Ontaneda

  3. Department of Neurological Sciences, Larner College of Medicine at the University of Vermont University Health Center, Arnold 2, 1 South Prospect Street, Burlington, VT, 05401, USA

    Andrew J. Solomon

Authors
  1. Marwa Kaisey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghazal Lashgari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justyna Fert-Bober
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Ontaneda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew J. Solomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nancy L. Sicotte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marwa Kaisey.

Ethics declarations

Conflicts of Interests

Marwa Kaisey MD: Consulting or Advisory boards for Alexion, Biogen, Genentech, and Novartis.

Ghazal Lashgari MD: The author does not have existing conflict of interest. Justyna Fert-Bober PhD: The author does not have existing conflict of interest.

Daniel Ontaneda MD, PhD: Research support from the National Institutes of Health, Department of Defense, National Multiple Sclerosis Society, Patient Centered Outcomes Research Institute, Race to Erase MS Foundation, Genentech, Genzyme, and Novartis. Consulting fees from Biogen Idec, Genentech/Roche, Genzyme, Janssen, Novartis, and Merck.

Andrew J. Solomon MD: Consulting or Advisory Boards: EMD Serono, Biogen, Alexion, Celgene, Genentech, Greenwich Biosciences, TG Therapeutics, Octave Bioscience; Non- promotional speaking: EMD Serono; Research Funding: Biogen, Bristol Myers Squibb; Contracted Research: Biogen, Novartis, Actelion, Genentech/Roche, Sanofi.

Nancy L. Sicotte MD: The author does not have existing conflict of interest.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Demyelinating Disorders

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaisey, M., Lashgari, G., Fert-Bober, J. et al. An Update on Diagnostic Laboratory Biomarkers for Multiple Sclerosis. Curr Neurol Neurosci Rep 22, 675–688 (2022). https://doi.org/10.1007/s11910-022-01227-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11910-022-01227-1

Keywords

Search

Navigation