Insight into Early Diagnosis of Multiple Sclerosis by Targeting Prognostic
Biomarkers

Nidhi      Puranik; Dhananjay      Yadav; Minseok      Song
doi:10.2174/0113816128247471231018053737
Abstract

Multiple sclerosis (MS) is a central nervous system (CNS) immune-mediated disease that mainly strikes young adults and leaves them disabled. MS is an autoimmune illness that causes the immune system to attack the brain and spinal cord. The myelin sheaths, which insulate the nerve fibers, are harmed by our own immune cells, and this interferes with brain signal transmission. Numbness, tingling, mood swings, memory problems, exhaustion, agony, vision problems, and/or paralysis are just a few of the symptoms. Despite technological advancements and significant research efforts in recent years, diagnosing MS can still be difficult. Each patient's MS is distinct due to a heterogeneous and complex pathophysiology with diverse types of disease courses. There is a pressing need to identify markers that will allow for more rapid and accurate diagnosis and prognosis assessments to choose the best course of treatment for each MS patient. The cerebrospinal fluid (CSF) is an excellent source of particular indicators associated with MS pathology. CSF contains molecules that represent pathological processes such as inflammation, cellular damage, and loss of blood-brain barrier integrity. Oligoclonal bands, neurofilaments, MS-specific miRNA, lncRNA, IgG-index, and anti-aquaporin 4 antibodies are all clinically utilised indicators for CSF in MS diagnosis. In recent years, a slew of new possible biomarkers have been presented. In this review, we look at what we know about CSF molecular markers and how they can aid in the diagnosis and differentiation of different MS forms and treatment options, and monitoring and predicting disease progression, therapy response, and consequences during such opportunistic infections.
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[1]
Khan S, Vandermorris A, Shepherd J, et al. Embracing uncertainty, managing complexity: Applying complexity thinking principles to transformation efforts in healthcare systems. BMC Health Serv Res  2018; 18(1): 192.
 [http://dx.doi.org/10.1186/s12913-018-2994-0] [PMID: 29562898]

[2]
Villoslada P. Biomarkers for multiple sclerosis. Drug News Perspect  2010; 23(9): 585-95.
 [http://dx.doi.org/10.1358/dnp.2010.23.9.1472300] [PMID: 21152453]

[3]
Dorsett M, Liang SY. Diagnosis and treatment of central nervous system infections in the emergency department. Emerg Med Clin North Am  2016; 34(4): 917-42.
 [http://dx.doi.org/10.1016/j.emc.2016.06.013] [PMID: 27741995]

[4]
Baldacci F, Mazzucchi S, Della Vecchia A, et al. The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases. Expert Rev Mol Diagn  2020; 20(4): 421-41.
 [http://dx.doi.org/10.1080/14737159.2020.1731306] [PMID: 32066283]

[5]
Gaetani L, Paolini Paoletti F, Bellomo G, et al. CSF and blood biomarkers in neuroinflammatory and neurodegenerative diseases: Implications for treatment. Trends Pharmacol Sci  2020; 41(12): 1023-37.
 [http://dx.doi.org/10.1016/j.tips.2020.09.011] [PMID: 33127098]

[6]
Palanichamy A, Apeltsin L, Kuo TC, et al. Immunoglobulin class-switched B cells form an active immune axis between CNS and periphery in multiple sclerosis. Sci Transl Med  2014; 6(248): 248ra106.
 [http://dx.doi.org/10.1126/scitranslmed.3008930]

[7]
Tavakolpour S. Towards personalized medicine for patients with autoimmune diseases: Opportunities and challenges. Immunol Lett  2017; 190: 130-8.
 [http://dx.doi.org/10.1016/j.imlet.2017.08.002] [PMID: 28797806]

[8]
Buyse M, Molenberghs G, Paoletti X, et al. Statistical evaluation of surrogate endpoints with examples from cancer clinical trials. Biom J  2016; 58(1): 104-32.
 [http://dx.doi.org/10.1002/bimj.201400049] [PMID: 25682941]

[9]
Katsavos S, Anagnostouli M. Biomarkers in multiple sclerosis: An up-to-date overview. Mult Scler Int  2013; 2013
 [http://dx.doi.org/10.1155/2013/340508]

[10]
 Sandmann T, Heskes T, Abbas S. Relevant biomarkers in the prediction of good and bad days for multiple sclerosis patients. Bachelor Thesis, Radboud University. 2016.

[11]
Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood)  2018; 243(3): 213-21.
 [http://dx.doi.org/10.1177/1535370217750088] [PMID: 29405771]

[12]
Anderson DC, Kodukula K. Biomarkers in pharmacology and drug discovery. Biochem Pharmacol  2014; 87(1): 172-88.
 [http://dx.doi.org/10.1016/j.bcp.2013.08.026] [PMID: 24001556]

[13]
Ottervald J, Franzén B, Nilsson K, et al. Multiple sclerosis: Identification and clinical evaluation of novel CSF biomarkers. J Proteomics  2010; 73(6): 1117-32.
 [http://dx.doi.org/10.1016/j.jprot.2010.01.004] [PMID: 20093204]

[14]
Macaron G, Ontaneda D. Diagnosis and management of progressive multiple sclerosis. Biomedicines  2019; 7(3): 56.
 [http://dx.doi.org/10.3390/biomedicines7030056] [PMID: 31362384]

[15]
Inojosa H, Proschmann U, Akgün K, Ziemssen T. A focus on secondary progressive multiple sclerosis (SPMS): Challenges in diagnosis and definition. J Neurol  2021; 268(4): 1210-21.
 [http://dx.doi.org/10.1007/s00415-019-09489-5] [PMID: 31363847]

[16]
Tremlett H, Yinshan Zhao , Devonshire V. Natural history of secondary-progressive multiple sclerosis. Mult Scler  2008; 14(3): 314-24.
 [http://dx.doi.org/10.1177/1352458507084264] [PMID: 18208898]

[17]
Lassmann H. Multiple sclerosis pathology. Cold Spring Harb Perspect Med  2018; 8(3): a028936.
 [http://dx.doi.org/10.1101/cshperspect.a028936] [PMID: 29358320]

[18]
Dobson R, Giovannoni G. Multiple sclerosis – A review. Eur J Neurol  2019; 26(1): 27-40.
 [http://dx.doi.org/10.1111/ene.13819] [PMID: 30300457]

[19]
Oh J, Vidal-Jordana A, Montalban X. Multiple sclerosis: Clinical aspects. Curr Opin Neurol  2018; 31(6): 752-9.
 [http://dx.doi.org/10.1097/WCO.0000000000000622] [PMID: 30300239]

[20]
Solaro C, Gamberini G, Masuccio FG. Depression in multiple sclerosis: Epidemiology, aetiology, diagnosis and treatment. CNS Drugs  2018; 32(2): 117-33.
 [http://dx.doi.org/10.1007/s40263-018-0489-5] [PMID: 29417493]

[21]
Kalb R, Beier M, Benedict RHB, et al. Recommendations for cognitive screening and management in multiple sclerosis care. Mult Scler  2018; 24(13): 1665-80.
 [http://dx.doi.org/10.1177/1352458518803785] [PMID: 30303036]

[22]
Baecher-Allan C, Kaskow BJ, Weiner HL. Multiple sclerosis: Mechanisms and immunotherapy. Neuron  2018; 97(4): 742-68.
 [http://dx.doi.org/10.1016/j.neuron.2018.01.021] [PMID: 29470968]

[23]
Goldenberg MM. Multiple sclerosis review. P&T  2012; 37(3): 175-84.
 [PMID: 22605909]

[24]
Natalizumab: AN 100226, anti-4alpha integrin monoclonal antibody. Drugs R D  2004; 5(2): 102-7.
 [http://dx.doi.org/10.2165/00126839-200405020-00007] [PMID: 15293871]

[25]
Multiple Sclerosis Agents. LiverTox: Clinical and Research Information on Drug-Induced Liver Injury. Bethesda, MD: National Institute of Diabetes and Digestive and Kidney Diseases 2012.

[26]
Garcia-Montojo M, Rodriguez-Martin E, Ramos-Mozo P, et al. Syncytin-1/HERV-W envelope is an early activation marker of leukocytes and is upregulated in multiple sclerosis patients. Eur J Immunol  2020; 50(5): 685-94.
 [http://dx.doi.org/10.1002/eji.201948423] [PMID: 32012247]

[27]
Chatterjee S, Haque R, Raza SS. Stem cells and gene therapy.   Handbook of Biotechnology. 503.

[28]
Chen W, Hu Y, Ju D. Gene therapy for neurodegenerative disorders: Advances, insights and prospects. Acta Pharm Sin B  2020; 10(8): 1347-59.
 [http://dx.doi.org/10.1016/j.apsb.2020.01.015] [PMID: 32963936]

[29]
Puranik N, Arukha AP, Yadav SK, Yadav D, Jin JO. Exploring the role of stem cell therapy in treating neurodegenerative diseases: Challenges and current perspectives. Curr Stem Cell Res Ther  2022; 17(2): 113-25.
 [http://dx.doi.org/10.2174/1574888X16666210810103838] [PMID: 35135462]

[30]
Harroud A, Stridh P, McCauley JL, et al. International Multiple Sclerosis Genetics Consortium; MultipleMS Consortium. Locus for severity implicates CNS resilience in progression of multiple sclerosis. Nature  2023; 619(7969): 323-31.
 [http://dx.doi.org/10.1038/s41586-023-06250-x] [PMID: 37380766]

[31]
Brownlee WJ, Hardy TA, Fazekas F, Miller DH. Diagnosis of multiple sclerosis: Progress and challenges. Lancet  2017; 389(10076): 1336-46.
 [http://dx.doi.org/10.1016/S0140-6736(16)30959-X] [PMID: 27889190]

[32]
Mathur D, Rout S, Mishra BK, et al. Potential pathological biomarkers in multiple sclerosis. Preprints  2020; 2020090293.
 [http://dx.doi.org/10.20944/preprints202009.0293.v1]

[33]
Walecki J, Barcikowska M, Ćwikła JB, Gabryelewicz T. N-acetylaspartate, choline, myoinositol, glutamine and glutamate (glx) concentration changes in proton MR spectroscopy (1H MRS) in patients with mild cognitive impairment (MCI). Med Sci Monit  2011; 17(12): MT105-11.
 [http://dx.doi.org/10.12659/MSM.882112] [PMID: 22129910]

[34]
Matthews PM, Datta G. Positron-emission tomography molecular imaging of glia and myelin in drug discovery for multiple sclerosis. Expert Opin Drug Discov  2015; 10(5): 557-70.
 [http://dx.doi.org/10.1517/17460441.2015.1032240] [PMID: 25843125]

[35]
Zhou Z, Xiong H, Xie F, Wu Z, Feng Y. A meta-analytic review of the value of miRNA for multiple sclerosis diagnosis. Front Neurol  2020; 11: 132.
 [http://dx.doi.org/10.3389/fneur.2020.00132] [PMID: 32158427]

[36]
Tiu VE, Enache I, Panea CA, Tiu C, Popescu BO. Predictive MRI biomarkers in MS-a critical review. Medicina (Kaunas)  2022; 58(3): 377.
 [http://dx.doi.org/10.3390/medicina58030377] [PMID: 35334554]

[37]
Toscano S, Patti F. CSF biomarkers in multiple sclerosis: Beyond neuroinflammation. Neuroimmunol Neuroinflamm  2021; 8(1): 14-41.

[38]
Deisenhammer F, Zetterberg H, Fitzner B, Zettl UK. The cerebrospinal fluid in multiple sclerosis. Front Immunol  2019; 10: 726.
 [http://dx.doi.org/10.3389/fimmu.2019.00726] [PMID: 31031747]

[39]
Harris V, Tuddenham J, Sadiq S. Biomarkers of multiple sclerosis: Current findings. Degener Neurol Neuromuscul Dis  2017; 7: 19-29.
 [http://dx.doi.org/10.2147/DNND.S98936] [PMID: 30050375]

[40]
Huang J, Khademi M, Fugger L, et al. Inflammation-related plasma and CSF biomarkers for multiple sclerosis. Proc Natl Acad Sci USA  2020; 117(23): 12952-60.
 [http://dx.doi.org/10.1073/pnas.1912839117] [PMID: 32457139]

[41]
Presslauer S, Milosavljevic D, Hübl W, et al. Validation of kappa free light chains as a diagnostic biomarker in multiple sclerosis and clinically isolated syndrome: A multicenter study. Mult Scler  2015; 22(4): 502-10.
 [PMID: 26199348]

[42]
Menéndez-Valladares P, García-Sánchez MI, Cuadri Benítez P, et al. Free kappa light chains in cerebrospinal fluid as a biomarker to assess risk conversion to multiple sclerosis. Mult Scler J Exp Transl Clin  2015; 1: 2055217315620935.
 [http://dx.doi.org/10.1177/2055217315620935] [PMID: 28607709]

[43]
Housley WJ, Pitt D, Hafler DA. Biomarkers in multiple sclerosis. Clin Immunol  2015; 161(1): 51-8.
 [http://dx.doi.org/10.1016/j.clim.2015.06.015] [PMID: 26143623]

[44]
Momtazmanesh S, Shobeiri P, Saghazadeh A, et al. Neuronal and glial CSF biomarkers in multiple sclerosis: A systematic review and meta-analysis. Rev Neurosci  2021; 32(6): 573-95.
 [http://dx.doi.org/10.1515/revneuro-2020-0145] [PMID: 33594840]

[45]
Floro S, Carandini T, Pietroboni AM, De Riz MA, Scarpini E, Galimberti D. Role of chitinase 3–like 1 as a biomarker in multiple sclerosis: A systematic review and meta-analysis.  Neurol: Neuroimmunol Neuroinflammation  2022; 9(4): e1164.

[46]
Du J, Yi M, Zhou F, et al. S100B is selectively expressed by gray matter protoplasmic astrocytes and myelinating oligodendrocytes in the developing CNS. Mol Brain  2021; 14(1): 154.
 [http://dx.doi.org/10.1186/s13041-021-00865-9] [PMID: 34615523]

[47]
Sorci G, Bianchi R, Riuzzi F, et al. S100B protein, a damage-associated molecular pattern protein in the brain and heart, and beyond. Cardiovasc Psychiatry Neurol  2010; 2010.

[48]
Barateiro A, Afonso V, Santos G, et al. S100B as a potential biomarker and therapeutic target in multiple sclerosis. Mol Neurobiol  2016; 53(6): 3976-91.
 [http://dx.doi.org/10.1007/s12035-015-9336-6] [PMID: 26184632]

[49]
Harris VK, Sadiq SA. Biomarkers of therapeutic response in multiple sclerosis: Current status. Mol Diagn Ther  2014; 18(6): 605-17.
 [http://dx.doi.org/10.1007/s40291-014-0117-0] [PMID: 25164543]

[50]
Kapoor R, Smith KE, Allegretta M, et al. Serum neurofilament light as a biomarker in progressive multiple sclerosis. Neurology  2020; 95(10): 436-44.
 [http://dx.doi.org/10.1212/WNL.0000000000010346] [PMID: 32675076]

[51]
Bittner S, Oh J, Havrdová EK, Tintoré M, Zipp F. The potential of serum neurofilament as biomarker for multiple sclerosis. Brain  2021; 144(10): 2954-63.
 [http://dx.doi.org/10.1093/brain/awab241] [PMID: 34180982]

[52]
Correale J, Farez MF. Does helminth activation of toll-like receptors modulate immune response in multiple sclerosis patients? Front Cell Infect Microbiol  2012; 2: 112.
 [http://dx.doi.org/10.3389/fcimb.2012.00112] [PMID: 22937527]

[53]
Hossain MJ, Morandi E, Tanasescu R, et al. The soluble form of toll-like receptor 2 is elevated in serum of multiple sclerosis patients: A novel potential disease biomarker. Front Immunol  2018; 9: 457.
 [http://dx.doi.org/10.3389/fimmu.2018.00457] [PMID: 29593720]

[54]
Keane RW, Dietrich WD, de Rivero Vaccari JP. Inflammasome proteins as biomarkers of multiple sclerosis. Front Neurol  2018; 9: 135.
 [http://dx.doi.org/10.3389/fneur.2018.00135] [PMID: 29615953]

[55]
Inoue M, Shinohara ML. Nlrp3 inflammasome and MS/EAE. Autoimmune Dis  2013; 2013.

[56]
Zhang HL, Wu J, Zhu J. The immune-modulatory role of apolipoprotein E with emphasis on multiple sclerosis and experimental autoimmune encephalomyelitis. Clin Dev Immunol  2010; 2010: 1-10.
 [http://dx.doi.org/10.1155/2010/186813] [PMID: 20613949]

[57]
Gebregiworgis T, Nielsen HH, Massilamany C, et al. A urinary metabolic signature for multiple sclerosis and neuromyelitis optica. J Proteome Res  2016; 15(2): 659-66.
 [http://dx.doi.org/10.1021/acs.jproteome.5b01111] [PMID: 26759122]

[58]
Gebregiworgis T, Massilamany C, Gangaplara A, et al. Potential of urinary metabolites for diagnosing multiple sclerosis. ACS Chem Biol  2013; 8(4): 684-90.
 [http://dx.doi.org/10.1021/cb300673e] [PMID: 23369377]

[59]
Dobson R, Topping J, Davis A, Thompson E, Giovannoni G. Cerebrospinal fluid and urinary biomarkers in multiple sclerosis. Acta Neurol Scand  2013; 128(5): 321-7.
 [http://dx.doi.org/10.1111/ane.12119] [PMID: 23465040]

[60]
Jafari A, Babajani A, Rezaei-Tavirani M. Multiple sclerosis biomarker discoveries by proteomics and metabolomics approaches. Biomark Insights  2021; 16: 11772719211013352.
 [http://dx.doi.org/10.1177/11772719211013352] [PMID: 34017167]

[61]
Devos D, Forzy G, de Seze J, et al. Silver stained isoelectrophoresis of tears and cerebrospinal fluid in multiple sclerosis. J Neurol  2001; 248(8): 672-5.
 [http://dx.doi.org/10.1007/PL00007833] [PMID: 11569895]

[62]
Herman S, Khoonsari PE, Tolf A, et al. Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis. Theranostics  2018; 8(16): 4477-90.
 [http://dx.doi.org/10.7150/thno.26249] [PMID: 30214633]

[63]
Cicalini I, Rossi C, Pieragostino D, et al. Integrated lipidomics and metabolomics analysis of tears in multiple sclerosis: An insight into diagnostic potential of lacrimal fluid. Int J Mol Sci  2019; 20(6): 1265.
 [http://dx.doi.org/10.3390/ijms20061265] [PMID: 30871169]

[64]
Pieragostino D, Lanuti P, Cicalini I, 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.
 [http://dx.doi.org/10.1016/j.jprot.2019.103403] [PMID: 31170500]

[65]
Manconi B, Liori B, Cabras T, et al. Top-down proteomic profiling of human saliva in multiple sclerosis patients. J Proteomics  2018; 187: 212-22.
 [http://dx.doi.org/10.1016/j.jprot.2018.07.019] [PMID: 30086402]

[66]
Ziemssen T, Akgün K, Brück W. Molecular biomarkers in multiple sclerosis. J Neuroinflammation  2019; 16(1): 272.
 [http://dx.doi.org/10.1186/s12974-019-1674-2] [PMID: 31870389]

[67]
Martin SJ, McGlasson S, Hunt D, Overell J. Cerebrospinal fluid neurofilament light chain in multiple sclerosis and its subtypes: A meta-analysis of case–control studies. J Neurol Neurosurg Psychiatry  2019; 90(9): 1059-67.
 [http://dx.doi.org/10.1136/jnnp-2018-319190] [PMID: 31123141]

[68]
Varhaug KN, Torkildsen Ø, Myhr KM, Vedeler CA. Neurofilament light chain as a biomarker in multiple sclerosis. Front Neurol  2019; 10: 338.
 [http://dx.doi.org/10.3389/fneur.2019.00338] [PMID: 31024432]

[69]
Olesen MN, Soelberg K, Debrabant B, 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.
 [http://dx.doi.org/10.1186/s12974-019-1440-5] [PMID: 30857557]

[70]
Domingues RB, Fernandes GBP, Leite FBVM, Senne C. Neurofilament light chain in the assessment of patients with multiple sclerosis. Arq Neuropsiquiatr  2019; 77(6): 436-41.
 [http://dx.doi.org/10.1590/0004-282x20190060] [PMID: 31314847]

[71]
Teunissen CE, Khalil M. Neurofilaments as biomarkers in multiple sclerosis. Mult Scler  2012; 18(5): 552-6.
 [http://dx.doi.org/10.1177/1352458512443092] [PMID: 22492131]

[72]
Cai L, Huang J. Neurofilament light chain as a biological marker for multiple sclerosis: A meta-analysis study. Neuropsychiatr Dis Treat  2018; 14: 2241-54.
 [http://dx.doi.org/10.2147/NDT.S173280] [PMID: 30214214]

[73]
Khalil M, Pirpamer L, Hofer E, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun  2020; 11(1): 812.
 [http://dx.doi.org/10.1038/s41467-020-14612-6] [PMID: 32041951]

[74]
Siller N, Kuhle J, Muthuraman M, et al. Serum neurofilament light chain is a biomarker of acute and chronic neuronal damage in early multiple sclerosis. Mult Scler  2019; 25(5): 678-86.
 [http://dx.doi.org/10.1177/1352458518765666] [PMID: 29542376]

[75]
Thebault S, Abdoli M, Fereshtehnejad SM, Tessier D, Tabard-Cossa V, Freedman MS. Serum neurofilament light chain predicts long term clinical outcomes in multiple sclerosis. Sci Rep  2020; 10(1): 10381.
 [http://dx.doi.org/10.1038/s41598-020-67504-6] [PMID: 32587320]

[76]
Bischof A, Manigold T, Barro C, et al. Serum neurofilament light chain: A biomarker of neuronal injury in vasculitic neuropathy. Ann Rheum Dis  2018; 77(7): 1093-4.
 [http://dx.doi.org/10.1136/annrheumdis-2017-212045] [PMID: 28743789]

[77]
Oldoni E, Smets I, Mallants K, et al. CHIT1 at diagnosis reflects long-term multiple sclerosis disease activity. Ann Neurol  2020; 87(4): 633-45.
 [http://dx.doi.org/10.1002/ana.25691] [PMID: 31997416]

[78]
Novakova L, Zetterberg H, Sundström P, et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology  2017; 89(22): 2230-7.
 [http://dx.doi.org/10.1212/WNL.0000000000004683] [PMID: 29079686]

[79]
Preziosa P, Rocca MA, Filippi M. Current state-of-art of the application of serum neurofilaments in multiple sclerosis diagnosis and monitoring. Expert Rev Neurother  2020; 20(8): 747-69.
 [http://dx.doi.org/10.1080/14737175.2020.1760846] [PMID: 32326770]

[80]
Puranik N, Yadav D, Yadav SK, Chavda VK, Jin JO. Proteomics and neurodegenerative disorders: Advancements in the diagnostic analysis. Curr Protein Pept Sci  2020; 21(12): 1174-83.
 [http://dx.doi.org/10.2174/1389203721666200511094222] [PMID: 32392110]

[81]
Hinsinger G, Galéotti N, Nabholz N, et al. Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis. Mult Scler  2015; 21(10): 1251-61.
 [http://dx.doi.org/10.1177/1352458514561906] [PMID: 25698171]

[82]
Fitzner B, Hecker M, Zettl UK. Molecular biomarkers in cerebrospinal fluid of multiple sclerosis patients. Autoimmun Rev  2015; 14(10): 903-13.
 [http://dx.doi.org/10.1016/j.autrev.2015.06.001] [PMID: 26071103]

[83]
Verberk IMW, Laarhuis MB, van den Bosch KA, et al. Serum glial fibrillary acidic protein and neurofilament light as prognostic biomarkers for clinical progression in subjective cognitive decline: The SCIENCe project. Alzheimers Dement  2020; 16(S5): e044783.
 [http://dx.doi.org/10.1002/alz.044783]

[84]
D’Ambrosio A, Pontecorvo S, Colasanti T, Zamboni S, Francia A, Margutti P. Peripheral blood biomarkers in multiple sclerosis. Autoimmun Rev  2015; 14(12): 1097-110.
 [http://dx.doi.org/10.1016/j.autrev.2015.07.014] [PMID: 26226413]

[85]
Corso G, Cristofano A, Sapere N, et al. Serum amino acid profiles in normal subjects and in patients with or at risk of alzheimer dementia. Dement Geriatr Cogn Disord Extra  2017; 7(1): 143-59.
 [http://dx.doi.org/10.1159/000466688] [PMID: 28626469]

[86]
Socha E, Koba M, Kośliński P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids  2019; 51(3): 367-71.
 [http://dx.doi.org/10.1007/s00726-019-02705-6] [PMID: 30725224]

[87]
Socha E, Kośliński P, Koba M, et al. Amino acid levels as potential biomarker of elderly patients with dementia. Brain Sci  2020; 10(12): 914.
 [http://dx.doi.org/10.3390/brainsci10120914] [PMID: 33260889]

[88]
Blesa J, Trigo-Damas I, Quiroga-Varela A, Jackson-Lewis VR. Oxidative stress and Parkinson’s disease. Front Neuroanat  2015; 9: 91.
 [http://dx.doi.org/10.3389/fnana.2015.00091] [PMID: 26217195]

[89]
Figura M, Kuśmierska K, Bucior E, et al. Serum amino acid profile in patients with Parkinson’s disease. PLoS One  2018; 13(1): e0191670.
 [http://dx.doi.org/10.1371/journal.pone.0191670] [PMID: 29377959]

[90]
Cocco E, Murgia F, Lorefice L, et al. 1H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis. Neurol Neuroimmunol Neuroinflamm  2016; 3(1): e185.
 [http://dx.doi.org/10.1212/NXI.0000000000000185] [PMID: 26740964]

[91]
Kasakin MF, Rogachev AD, Predtechenskaya EV, Zaigraev VJ, Koval VV, Pokrovsky AG. Changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls.  Mult Scler Inf  2020; 2020.
 [http://dx.doi.org/10.1155/2020/9010937]

[92]
Poser CM, Sylwester DL, Ho B, Alpert A. Amino acid residues of serum and CSF protein in multiple sclerosis. Clinical application of statistical discriminant analysis. Arch Neurol  1975; 32(5): 308-14.
 [http://dx.doi.org/10.1001/archneur.1975.00490470052007] [PMID: 1094992]

[93]
Berghoff SA, Spieth L, Saher G. Local cholesterol metabolism orchestrates remyelination. Trends Neurosci  2022; 45(4): 272-83.
 [http://dx.doi.org/10.1016/j.tins.2022.01.001] [PMID: 35153084]

[94]
Ferreira HB, Melo T, Monteiro A, Paiva A, Domingues P, Domingues MR. Serum phospholipidomics reveals altered lipid profile and promising biomarkers in multiple sclerosis. Arch Biochem Biophys  2021; 697: 108672.
 [http://dx.doi.org/10.1016/j.abb.2020.108672] [PMID: 33189653]

[95]
Vejux A, Ghzaiel I, Nury T, et al. Oxysterols and multiple sclerosis: Physiopathology, evolutive biomarkers and therapeutic strategy. J Steroid Biochem Mol Biol  2021; 210: 105870.
 [http://dx.doi.org/10.1016/j.jsbmb.2021.105870] [PMID: 33684483]

[96]
Raphael I, Forsthuber T. Identification of predictive protein biomarkers for treatment efficacy and clinical relapses of multiple sclerosis (THER7P.950). J Immunol  2015; 194(1 Supplement): 208-10.

[97]
Quintana FJ, Yeste A, Weiner HL, Covacu R. Lipids and lipid-reactive antibodies as biomarkers for multiple sclerosis. J Neuroimmunol  2012; 248(1-2): 53-7.
 [http://dx.doi.org/10.1016/j.jneuroim.2012.01.002] [PMID: 22579051]

[98]
Bakshi R, Yeste A, Patel B, et al. Serum lipid antibodies are associated with cerebral tissue damage in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm  2016; 3(2): e200.
 [http://dx.doi.org/10.1212/NXI.0000000000000200] [PMID: 26894204]

[99]
Nogueras L, Gonzalo H, Jové M, et al. Lipid profile of cerebrospinal fluid in multiple sclerosis patients: A potential tool for diagnosis. Sci Rep  2019; 9(1): 11313.
 [http://dx.doi.org/10.1038/s41598-019-47906-x] [PMID: 31383928]

[100]
Peschl P, Schanda K, Zeka B, et al. Human antibodies against the myelin oligodendrocyte glycoprotein can cause complement-dependent demyelination. J Neuroinflammation  2017; 14(1): 208.
 [http://dx.doi.org/10.1186/s12974-017-0984-5] [PMID: 29070051]

[101]
Srivastava R, Aslam M, Kalluri SR, et al. Potassium channel KIR4.1 as an immune target in multiple sclerosis. N Engl J Med  2012; 367(2): 115-23.
 [http://dx.doi.org/10.1056/NEJMoa1110740] [PMID: 22784115]

[102]
Paul A, Comabella M, Gandhi R. Biomarkers in multiple sclerosis. Cold Spring Harb Perspect Med  2019; 9(3): a029058.
 [http://dx.doi.org/10.1101/cshperspect.a029058] [PMID: 29500303]

[103]
DeMarshall C, Goldwaser EL, Sarkar A, et al. Autoantibodies as diagnostic biomarkers for the detection and subtyping of multiple sclerosis. J Neuroimmunol  2017; 309: 51-7.
 [http://dx.doi.org/10.1016/j.jneuroim.2017.05.010] [PMID: 28601288]

[104]
Yu X, Graner M, Kennedy PGE, Liu Y. The role of antibodies in the pathogenesis of multiple sclerosis. Front Neurol  2020; 11: 533388.
 [http://dx.doi.org/10.3389/fneur.2020.533388] [PMID: 33192968]

[105]
Gharesouran J, Taheri M, Sayad A, Ghafouri-Fard S, Mazdeh M, Omrani MD. 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.
 [http://dx.doi.org/10.1007/s12031-018-1248-2] [PMID: 30610590]

[106]
Piket E, Zheleznyakova GY, Kular L, Jagodic M. Small non-coding RNAs as important players, biomarkers and therapeutic targets in multiple sclerosis: A comprehensive overview. J Autoimmun  2019; 101: 17-25.
 [http://dx.doi.org/10.1016/j.jaut.2019.04.002] [PMID: 31014917]

[107]
Yang X, Wu Y, Zhang B, Ni B. Noncoding RNAs in multiple sclerosis. Clin Epigenetics  2018; 10(1): 149.
 [http://dx.doi.org/10.1186/s13148-018-0586-9] [PMID: 30497529]

[108]
Elkhodiry AA, El Tayebi HM. Scavenging the hidden impacts of non-coding RNAs in multiple sclerosis. Noncoding RNA Res  2021; 6(4): 187-99.
 [http://dx.doi.org/10.1016/j.ncrna.2021.12.002] [PMID: 34938929]

[109]
Mycko MP, Baranzini SE. microRNA and exosome profiling in multiple sclerosis. Mult Scler  2020; 26(5): 599-604.
 [http://dx.doi.org/10.1177/1352458519879303] [PMID: 31965891]

[110]
Yousuf A, Qurashi A. Non-coding RNAs in the pathogenesis of multiple sclerosis. Front Genet  2021; 12: 717922.
 [http://dx.doi.org/10.3389/fgene.2021.717922] [PMID: 34659340]

[111]
Jalaiei A, Asadi MR, Sabaie H, et al. Long Non-Coding RNAs, novel offenders or guardians in multiple sclerosis: A scoping review. Front Immunol  2021; 12: 774002.
 [http://dx.doi.org/10.3389/fimmu.2021.774002] [PMID: 34950142]

[112]
Lodde V, Murgia G, Simula ER, Steri M, Floris M, Idda ML. Long noncoding RNAs and circular RNAs in autoimmune diseases. Biomolecules  2020; 10(7): 1044.
 [http://dx.doi.org/10.3390/biom10071044] [PMID: 32674342]

[113]
Peplow PV, Martinez B. MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis. Neural Regen Res  2020; 15(10): 1831-7.
 [http://dx.doi.org/10.4103/1673-5374.280307] [PMID: 32246624]

[114]
Roy B, Lee E, Li T, Rampersaud M. Role of miRNAs in neurodegeneration: From disease cause to tools of biomarker discovery and therapeutics. Genes (Basel)  2022; 13(3): 425.
 [http://dx.doi.org/10.3390/genes13030425] [PMID: 35327979]

[115]
Xia X, Wang Y, Huang Y, Zhang H, Lu H, Zheng JC. Exosomal miRNAs in central nervous system diseases: Biomarkers, pathological mediators, protective factors and therapeutic agents. Prog Neurobiol  2019; 183: 101694.
 [http://dx.doi.org/10.1016/j.pneurobio.2019.101694] [PMID: 31542363]

[116]
Mansoor SR, Ghasemi-Kasman M, Yavarpour-Bali H. The role of microRNAs in multiple sclerosis. Int Rev Immunol  2022; 41(2): 57-71.
 [http://dx.doi.org/10.1080/08830185.2020.1826474] [PMID: 32997552]

[117]
Peplow PV, Martinez B. MicroRNAs in blood and cerebrospinal fluid as diagnostic biomarkers of multiple sclerosis and to monitor disease progression. Neural Regen Res  2020; 15(4): 606-19.
 [http://dx.doi.org/10.4103/1673-5374.266905] [PMID: 31638082]

[118]
Haghikia A, Haghikia A, Hellwig K, et al. Regulated microRNAs in the CSF of patients with multiple sclerosis: A case-control study. Neurology  2012; 79(22): 2166-70.
 [http://dx.doi.org/10.1212/WNL.0b013e3182759621] [PMID: 23077021]

[119]
Vistbakka J, Elovaara I, Lehtimäki T, Hagman S. Circulating microRNAs as biomarkers in progressive multiple sclerosis. Mult Scler  2017; 23(3): 403-12.
 [http://dx.doi.org/10.1177/1352458516651141] [PMID: 27246141]

[120]
Lee G. The balance of Th17 versus Treg cells in autoimmunity. Int J Mol Sci  2018; 19(3): 730.
 [http://dx.doi.org/10.3390/ijms19030730] [PMID: 29510522]

[121]
Martinez GJ, Nurieva RI, Yang XO, Dong C. Regulation and function of proinflammatory TH17 cells. Ann N Y Acad Sci  2008; 1143(1): 188-211.
 [http://dx.doi.org/10.1196/annals.1443.021] [PMID: 19076351]

[122]
Dolati S, Marofi F, Babaloo Z, et al. Dysregulated network of miRNAs involved in the pathogenesis of multiple sclerosis. Biomed Pharmacother  2018; 104: 280-90.
 [http://dx.doi.org/10.1016/j.biopha.2018.05.050] [PMID: 29775896]

[123]
Gandhi R. miRNA in multiple sclerosis: Search for novel biomarkers. Mult Scler  2015; 21(9): 1095-103.
 [http://dx.doi.org/10.1177/1352458515578771] [PMID: 25921051]

[124]
Pietrasik S, Dziedzic A, Miller E, Starosta M, Saluk-Bijak J. Circulating miRNAs as potential biomarkers distinguishing relapsing–remitting from secondary progressive multiple sclerosis. A review. Int J Mol Sci  2021; 22(21): 11887.
 [http://dx.doi.org/10.3390/ijms222111887] [PMID: 34769314]

[125]
Kramer S, Haghikia A, Bang C, et al. Elevated levels of miR-181c and miR-633 in the CSF of patients with MS. Neurol Neuroimmunol Neuroinflamm  2019; 6(6): e623.
 [http://dx.doi.org/10.1212/NXI.0000000000000623] [PMID: 31575652]

[126]
Muñoz-San Martín M, Reverter G, Robles-Cedeño R, et al. Analysis of miRNA signatures in CSF identifies upregulation of miR-21 and miR-146a/b in patients with multiple sclerosis and active lesions. J Neuroinflammation  2019; 16(1): 220.
 [http://dx.doi.org/10.1186/s12974-019-1590-5] [PMID: 31727077]

[127]
Minutti-Zanella C, Bojalil-Álvarez L, García-Villaseñor E, et al. miRNAs in multiple sclerosis: A clinical approach. Mult Scler Relat Disord  2022; 63: 103835.
 [http://dx.doi.org/10.1016/j.msard.2022.103835] [PMID: 35533548]

[128]
Zhang Y, Liu Y, Liu H, Tang WH. Exosomes: Biogenesis, biologic function and clinical potential. Cell Biosci  2019; 9(1): 19.
 [http://dx.doi.org/10.1186/s13578-019-0282-2] [PMID: 30815248]

[129]
Zhou B, Xu K, Zheng X, et al. Application of exosomes as liquid biopsy in clinical diagnosis. Signal Transduct Target Ther  2020; 5(1): 144.
 [http://dx.doi.org/10.1038/s41392-020-00258-9] [PMID: 32747657]

[130]
Manna I, De Benedittis S, Quattrone A, Maisano D, Iaccino E, Quattrone A. Exosomal miRNAs as potential diagnostic biomarkers in Alzheimer’s disease. Pharmaceuticals (Basel)  2020; 13(9): 243.
 [http://dx.doi.org/10.3390/ph13090243] [PMID: 32932746]

[131]
Komlakh K, Aghamiri SH, Farshadmoghadam H. The role and therapeutic applications of exosomes in multiple sclerosis disease. Clin Exp Pharmacol Physiol  2022; 49(12): 1249-56.
 [http://dx.doi.org/10.1111/1440-1681.13710] [PMID: 35918850]

[132]
Petracca M, Pontillo G, Moccia M, et al. Neuroimaging correlates of cognitive dysfunction in adults with multiple sclerosis. Brain Sci  2021; 11(3): 346.
 [http://dx.doi.org/10.3390/brainsci11030346] [PMID: 33803287]

[133]
Inglese M, Oesingmann N, Casaccia P, Fleysher L. Progressive multiple sclerosis and gray matter pathology: An MRI perspective. Mt Sinai J Med  2011; 78(2): 258-67.
 [http://dx.doi.org/10.1002/msj.20247] [PMID: 21425269]

[134]
Zhu L, Chen D, Lu X, et al. An ultrasensitive flow cytometric immunoassay based on bead surface-initiated template-free DNA extension. Chem Sci (Camb)  2018; 9(32): 6605-13.
 [http://dx.doi.org/10.1039/C8SC02752H] [PMID: 30310592]

[135]
Huang LK, Chao SP, Hu CJ. Clinical trials of new drugs for Alzheimer disease. J Biomed Sci  2020; 27(1): 18.
 [http://dx.doi.org/10.1186/s12929-019-0609-7] [PMID: 31906949]

[136]
Comabella M, Sastre-Garriga J, Montalban X. Precision medicine in multiple sclerosis. Curr Opin Neurol  2016; 29(3): 254-62.
 [http://dx.doi.org/10.1097/WCO.0000000000000336] [PMID: 27075495]

[137]
Sapko K, Jamroz-Wiśniewska A, Marciniec M, Kulczyński M, Szczepańska-Szerej A, Rejdak K. Biomarkers in multiple sclerosis: A review of diagnostic and prognostic factors. Neurol Neurochir Pol  2020; 54(3): 252-8.
 [http://dx.doi.org/10.5603/PJNNS.a2020.0037] [PMID: 32462652]

[138]
Mayeux R. Biomarkers: Potential uses and limitations. NeuroRx  2004; 1(2): 182-8.
 [http://dx.doi.org/10.1602/neurorx.1.2.182] [PMID: 15717018]

[139]
Kahn M, Schuierer L, Bartenschlager C, et al. Performance of antigen testing for diagnosis of COVID-19: A direct comparison of a lateral flow device to nucleic acid amplification based tests. BMC Infect Dis  2021; 21(1): 798.
 [http://dx.doi.org/10.1186/s12879-021-06524-7] [PMID: 34376187]
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DOI https://dx.doi.org/10.2174/0113816128247471231018053737	Print ISSN 1381-6128
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Insight into Early Diagnosis of Multiple Sclerosis by Targeting Prognostic Biomarkers

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