Supplementary Table 1. Summary of discussed studies on biomarkers for predicting conversion to MS and MS diagnosis
Biomarker
CXCL13
Cohorts of the initial
Novel information
Cohorts of the confirmation studies
study (number of
obtained in 2012–
(number of patients)
patients)
2015
Early MS conversion
CIS-CIS follow-up
Confirmation of initial
CIS n = 69, RRMS n = 389 SPMS n = 54
to CDMS1,2
n = 46, CIS-MS
clinical purpose3
PPMS n = 28, OIND n = 223, CIS n = 56, HC
Clinical purpose
n = 273
follow-up n = 45,
tension headache
controls n = 301
CIS n = 79, RRMS
n = 323, SPMS = 40,
PPMS n = 24, OND
n = 181, OIND
n = 1762
IgM OCB
Early MS conversion
MS-OCB+ n = 29, MS-
Confirmation of initial
to CDMS4–7
OCB- n = 525
clinical purpose10,11
CIS follow-up n = 20510, CIS follow-up n = 2411
Cohort 1 PPMS n = 80, Cohort 2 PPMS n = 678
RRMS follow-up
n=
298
RRMS n = 21,
PPMS subgroup
identification8
PPMS n = 1, CDMS
n = 549
KFLC
Diagnosis of early
CIS n = 3, CDMS
n = 18, non-MS
Confirmation of initial
CIS n = 69, RRMS n = 60, PPMS n = 5, OIND
MS12–14
neurological diseases
clinical purpose15,17
n = 8915
n = 3715
CIS-CIS follow-up n = 38, CIS-MS follow-up
CIS-MS n = 15, MS
n = 39, OIND n = 77, viral/bacterial infections
n = 33, OIND n = 8
n = 2017
OND n = 3316 CIS-MS
n = 29, MS n = 70,
non-CNS control
n = 45, OIND n = 7817
MRZ reaction
Diagnosis and
MS
n = 42,
NMO
prognosis of early
n = 209
CIS-CIS
MS9,18,19
n = 40,
CIS-MS
Confirmation of initial
MS n = 47, siblings OCB+ n = 9, siblings OCB-
clinical purpose20–22
n = 37 HC n = 5020 CIS n = 7 RRMS n = 6121
non-CNS-autoimmune neurological disorders
n = 4918
n = 37, OIND n = 16, RRMS n = 26, SPMS
n = 12, PPMS n = 822
MS
n = 42,
paraneoplastic
neurological disorder
n = 3419
CHI3L1
Early MS conversion
to
CDMS23–25
CIS-CIS n = 30, CISMS n =
3024
Confirmation of initial
clinical
purpose26–28
RRMS n = 156, SPMS n = 30, PPMS n = 66,
HC n = 5726 cohort 1 RRMS n = 21, control
n = 21, cohort 2 RRMS n = 21, control n = 21,
RRMS n = 24, SPMS
n = 24, NMO n = 12,
Progression29
cohort 3 CIS n = 40, RRMS n = 38, progressive
MS n = 16, control n = 2928
OIND n = 24, HC
n = 2423
CIS follow up n = 8627
CIS = 109, RRMS n = 19229
MS n = 0, Alzheimer
disease n = 10, ALS
n = 10, stroke n = 10,
HC n = 1925
NfL
Diagnosis and
RRMS n = 5,
prognosis for early
Alzheimer diseases
MS30,31
n = 5, ALS n = 5,
Confirmation of initial
clinical
purpose32–35
CIS-CIS n = 23, CIS-MS n = 19, CDMS n = 23
OND/HC n = 3,267
CIS-CIS n = 98, CIS-MS n = 10035
vascular dementia
n = 531
CIS n = 67, OND n = 1834
CIS n = 38, RRMS
CIS n = 62, RRMS n = 38, SPMS n = 25,
n = 42, SPMS n = 28,
PPMS n = 23, controls n = 7232
PPMS n = 6, nonCNS controls n = 28,
OND n = 18, OIND
n = 3930
miRNA-20a-5p
Diagnosis of early
RRMS n = 24, SPMS
Confirmation of initial
MS36
n = 17, PPMS n = 18,
clinical purpose37
CIS n = 25, RRMS n = 25, HC n = 5037
HC n = 3736
miRNA-22-5p
*Anti-KIR4.1
Diagnosis of early
CIS n = 25, RRMS
Confirmation of initial
CIS n = 25, RRMS n = 25, HC n = 5037 MS
MS37
n = 25, HC n = 5037
clinical purpose37,38
n = 4, HC n = 438
.
.
Diagnosis of a subset
cohort 1: CIS n = 44, RRMS n = 49, SPMS
of early MS39–41
n = 19, PPMS n = 10, OND n = 77, HC n = 14
.
.
.
Differential diagnosis
Cohort 2: CIS n = 53, RRMS n = 91, SPMS
MS-NMO40
n = 1, PPMS n = 1, OND n = 130 HC n = 8
Cohort 3 CIS n = 49, RRMS n = 77, OND
n = 12839
MS n = 286, HC n = 99, OND n = 10941
MS n = 268, OND n = 46, HC n = 4540
Blue background indicates a CSF biomarker, purple indicates a CSF+blood biomarker, and red indicates a blood biomarker. *) Inconclusive results
Abbreviations: ALS; amyotrophic lateral sclerosis, Anti KIR4.1 Anti-glial inward rectifying potassium channel 4.1, CDMS; clinical definite multiple
sclerosis , CHI3L1, chitinase-3-like protein 1, CIS; clinical isolated syndrome, CNS; central nervous system, CSF; cerebrospinal fluid, CXCL13, CXC
motif chemokine 13, HC; healthy controls, IgG, immunoglobulin G, IgM; immunoglobulin M, KFLC, kappa free light chains, miRNA, micro RNA,
MRZ, measles, rubella, zoster, MS; multiple sclerosis, NfL; neurofilament light chain; NMO; neuromyelitis optica, OCBs; oligoclonal bands, OIND;
other inflammatory neurological diseases OND; other neurological diseases, PPMS; primary progressive multiple sclerosis , RRMS; relapsing
remitting multiple sclerosis, SPMS; secondary progressive multiple sclerosis.
1.
Brettschneider, J. et al. The chemokine CXCL13 is a prognostic marker in clinically isolated syndrome (CIS). PLoS.One. 5, e11986 (2010).
2.
Khademi, M. et al. Cerebrospinal fluid CXCL13 in multiple sclerosis: a suggestive prognostic marker for the disease course. Mult.Scler. 17, 335–343
3.
Khademi, M. et al. Intense inflammation and nerve damage in early multiple sclerosis subsides at older age: a reflection by cerebrospinal fluid
biomarkers. PLoS One 8, e63172 (2013).
4.
Villar, L. M. et al. Intrathecal IgM synthesis predicts the onset of new relapses and a worse disease course in MS. Neurology 59, 555–559 (2002).
5.
Villar, L. M. et al. Intrathecal IgM synthesis is a prognostic factor in multiple sclerosis. Ann. Neurol. 53, 222–226 (2003).
6.
Villar, L. M. Influence of oligoclonal IgM specificity in multiple. 183–187 (2008).
7.
Villar, L. M. et al. Intrathecal synthesis of oligoclonal IgM against myelin lipids predicts an aggressive disease course in MS. J. Clin. Invest. 115, 187–
194 (2005).
8.
Villar, L. M. et al. Immunoglobulin M oligoclonal bands: biomarker of targetable inflammation in primary progressive multiple sclerosis. Ann. Neurol.
76, 231–40 (2014).
9.
Brettschneider, J. et al. IgG antibodies against measles, rubella, and varicella zoster virus predict conversion to multiple sclerosis in clinically isolated
syndrome. PLoS One 4, e7638 (2009).
10.
Ferraro, D. et al. Cerebrospinal fluid oligoclonal IgM bands predict early conversion to clinically definite multiple sclerosis in patients with clinically
isolated syndrome. J. Neuroimmunol. 257, 76–81 (2013).
11.
Magraner, M. J. et al. Brain atrophy and lesion load are related to CSF lipid-specific IgM oligoclonal bands in clinically isolated syndromes.
Neuroradiology 54, 5–12 (2012).
12.
Desplat-Jégo, S. et al. Quantification of immunoglobulin free light chains in cerebrospinal fluid by nephelometry. J. Clin. Immunol. 25, 338–345
(2005).
13.
Kaplan, B., Aizenbud, B. M., Golderman, S., Yaskariev, R. & Sela, B. A. Free light chain monomers in the diagnosis of multiple sclerosis. J.
Neuroimmunol. 229, 263–271 (2010).
14.
Presslauer, S., Milosavljevic, D., Brücke, T., Bayer, P. & Hübl, W. Elevated levels of kappa free light chains in CSF support the diagnosis of multiple
sclerosis. J. Neurol. 255, 1508–1514 (2008).
15.
Senel, M. et al. Cerebrospinal fluid immunoglobulin kappa light chain in clinically isolated syndrome and multiple sclerosis. PLoS One 9, e88680
(2014).
16.
Goffette, S., Schluep, M., Henry, H., Duprez, T. & Sindic, C. J. M. Detection of oligoclonal free kappa chains in the absence of oligoclonal IgG in the
CSF of patients with suspected multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 75, 308–310 (2004).
17.
Presslauer, S. et al. Kappa free light chains: diagnostic and prognostic relevance in MS and CIS. PLoS One 9, e89945 (2014).
18.
Jarius, S. et al. The intrathecal, polyspecific antiviral immune response: Specific for MS or a general marker of CNS autoimmunity? J. Neurol. Sci. 280,
98–100 (2009).
19.
Jarius, S. et al. Polyspecific, antiviral immune response distinguishes multiple sclerosis and neuromyelitis optica. J. Neurol. Neurosurg. Psychiatry 79,
1134–1136 (2008).
20.
Persson, L. et al. Elevated antibody reactivity to measles virus NCORE protein among patients with multiple sclerosis and their healthy siblings with
intrathecal oligoclonal immunoglobulin G production. J. Clin. Virol. 61, 107–112 (2014).
21.
Rosche, B. et al. Measles IgG antibody index correlates with T2 lesion load on MRI in patients with early multiple sclerosis. PLoS One 7, 1–4 (2012).
22.
Brecht, I. et al. Intrathecal, polyspecific antiviral immune response in oligoclonal band negative multiple sclerosis. PLoS One 7, 1–5 (2012).
23.
Correale, J. & Fiol, M. Chitinase effects on immune cell response in neuromyelitis optica and multiple sclerosis. Mult.Scler. 17, 521–531
24.
Bonneh-Barkay, D., Wang, G., Starkey, A., Hamilton, R. L. & Wiley, C. a. In vivo CHI3L1 (YKL-40) expression in astrocytes in acute and chronic
neurological diseases. J. Neuroinflammation 7, 34 (2010).
25.
Comabella, M. et al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain 133, 1082–93 (2010).
26.
Canto, E. et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain (2015). doi:10.1093/brain/awv017
27.
Modvig, S. et al. Cerebrospinal fluid levels of chitinase 3-like 1 and neurofilament light chain predict multiple sclerosis development and disability
after optic neuritis. Mult. Scler. J. 1–10 (2015). doi:10.1177/1352458515574148
28.
Hinsinger, G. et al. Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis. Mult. Scler. (2015).
doi:10.1177/1352458514561906
29.
Martínez, M. A. M. et al. Glial and neuronal markers in cerebrospinal fluid predict progression in multiple sclerosis. Mult. Scler. (2015).
doi:10.1177/1352458514549397
30.
Norgren, N., Rosengren, L. & Stigbrand, T. Elevated neurofilament levels in neurological diseases. Brain Res. 987, 25–31 (2003).
31.
Teunissen, C. E. et al. Combination of CSF N-acetylaspartate and neurofilaments in multiple sclerosis. Neurology 72, 1322–1329 (2009).
32.
Disanto, G. et al. Serum neurofilament light chain levels are increased in patients with a clinically isolated syndrome. J. Neurol. Neurosurg. Psychiatry
1–4 (2015). doi:10.1136/jnnp-2014-309690
33.
Fialová, L. et al. Serum and cerebrospinal fluid light neurofilaments and antibodies against them in clinically isolated syndrome and multiple
sclerosis. J. Neuroimmunol. 262, 113–20 (2013).
34.
Khalil, M. et al. CSF neurofilament and N-acetylaspartate related brain changes in clinically isolated syndrome. Mult. Scler. 19, 436–42 (2013).
35.
Kuhle, J. et al. A comparative study of CSF neurofilament light and heavy chain protein in MS. Mult. Scler. 19, 1597–603 (2013).
36.
Cox, M. B. et al. MicroRNAs miR-17 and miR-20a inhibit T cell activation genes and are under-expressed in MS whole blood. PLoS.One. 5, e12132–
(2010).
37.
Keller, A. et al. Comprehensive analysis of microRNA profiles in multiple sclerosis including next-generation sequencing. Mult. Scler. 20, 295–303
(2014).
38.
Siegel, S. R., Mackenzie, J., Chaplin, G., Jablonski, N. G. & Griffiths, L. Circulating microRNAs involved in multiple sclerosis. Mol. Biol. Rep. 39, 6219–25
(2012).
39.
Srivastava, R. et al. Potassium Channel KIR4.1 as an Immune Target in Multiple Sclerosis. N. Engl. J. Med. 367, 115–123 (2012).
40.
Brill, L. et al. Increased anti-KIR4.1 antibodies in multiple sclerosis: Could it be a marker of disease relapse? Mult. Scler. J. 4, 1–8 (2014).
41.
Nerrant, E. et al. Lack of confirmation of anti-inward rectifying potassium channel 4.1 antibodies as reliable markers of multiple sclerosis. Mult. Scler.
1352458514531086– (2014). doi:10.1177/1352458514531086