Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis

Journal Pre-proof Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis

Lorenzo Gaetani, Marinella Di Carlo, Brachelente Giovanni, Federico Valletta, Paolo Eusebi, Andrea Mancini, Lucia Gentili, Angela Borrelli, Paolo Calabresi, Paola Sarchielli, Carla Ferri, Alfredo Villa, Massimiliano Di Filippo PII:

S0165-5728(19)30403-5

DOI:

https://doi.org/10.1016/j.jneuroim.2019.577108

Reference:

JNI 577108

To appear in:

Journal of Neuroimmunology

Received date:

16 August 2019

Revised date:

28 October 2019

Accepted date:

4 November 2019

Please cite this article as: L. Gaetani, M. Di Carlo, B. Giovanni, et al., Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis, Journal of Neuroimmunology (2018), https://doi.org/10.1016/j.jneuroim.2019.577108

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© 2018 Published by Elsevier.

Journal Pre-proof Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis Lorenzo Gaetani MD 1 *, Marinella Di Carlo 2 *, Brachelente Giovanni MD 2 , Federico Valletta MD 1 , Paolo Eusebi PhD 1 , Andrea Mancini MD 1 , Lucia Gentili MD 1 , Angela Borrelli MD 1 , Paolo Calabresi MD, PhD 1,3 , Paola Sarchielli MD, PhD 1 , Carla Ferri MD 2 , Alfredo Villa MD 2 , Massimiliano Di Filippo MD, PhD

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IRCCS Fondazione Santa Lucia, Rome, Italy

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Clinical Pathology Laboratory, S. Maria della Misericordia Hospital, Perugia, Italy

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Corresponding author: Lorenzo Gaetani, MD

Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy.

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Santa Maria della Misericordia Hospital, 06132, Perugia, Italy

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Phone: 0039 075 5784228. Fax: 0039 075 5784229 E-mail address: [email protected] Abstract

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Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy

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* These authors equally contributed

Cerebrospinal fluid (CSF) free light chains (FLC) may be an alternative biomarker to oligoclonal bands (OCB) in multiple sclerosis (MS). Herein, we compared the diagnostic accuracy of CSF OCB and FLC and we tested the prognostic value of FLC in a cohort of 64 MS patients and 106 controls. A -index > 7.83 was more sensitive but less specific than OCB in discriminating MS patients from controls. Additionally, a -index > 10.61 performed better than OCB in the discrimination between MS and controls with inflammatory neurological diseases (p<0.001). In clinically isolated syndrome (CIS) patients, a -index > 10.61 significantly predicted time to conversion to MS (p=0.020). -index might be a valid alternative to OCB as a diagnostic biomarker for MS and might also be a prognostic marker in CIS.

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Keywords: Free light chains; kappa index; cerebrospinal fluid; biomarker; multiple sclerosis.

1- Introduction The diagnosis of multiple sclerosis (MS) relies on the demonstration of dissemination in time and space of the demyelinating lesions, by means of clinical and radiological findings, in the absence of better explanations. Following the last revision of the diagnostic criteria for MS, after the first clinical episode of the disease (i.e. the clinically isolated syndrome or CIS), the demonstration of intrathecal

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synthesis of immunoglobulins G (IgG) can substitute for the clinical or neuroradiological evidence of dissemination in time, thus allowing for an earlier diagnosis (Thompson et al., 2018). So far, the gold

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standard for demonstrating intrathecal IgG synthesis is represented by the detection of IgG oligoclonal

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bands (OCB) in the cerebrospinal fluid (CSF) by isoelectrofocusing (IEF) followed by

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immunoblotting or immunofixation (Petzold, 2013). However, the detection of OCB in CSF has several limits. First of all, it requires a time-consuming and expensive laboratory technique, with about

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three hours needed to obtain results (Presslauer et al., 2016). Furthermore, it only provides a

2005).

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qualitative, and not quantitative, information about the presence of IgG in CSF (Freedman et al.,

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For these reasons, since the late ‘70s, several research groups tried to assess the diagnostic value of  and  free light chains (FLC) in MS (Bracco et al., 1987; Rudick et al., 1985; Vandvik, 1977). Indeed, while synthesizing immunoglobulins, plasma cells also produce an excess of FLC that do not bind to heavy chains for antibody synthesis. During the course of central nervous system (CNS) inflammatory diseases, FLC are secreted in the CSF and their concentration might reflect the degree of intrathecal B cells and plasma cells activation (Duranti et al., 2013). The most reliable parameter used to assess FLC diagnostic value in MS is the -index, since it takes into account CSF and serum FLC concentrations together with CSF and serum albumin concentrations, thus adjusting FLC intrathecal synthesis by blood-brain barrier (BBB) dysfunction (Presslauer et al., 2008). Some recent studies proposed FLC measurement as a diagnostic biomarker for MS, since their sensitivity and specificity have been shown to be similar to those of OCB

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Journal Pre-proof (Presslauer et al., 2016, 2008; Senel et al., 2014; Valencia-Vera et al., 2018). However, the absence of universally established cut-off values, exhorts to be cautious in considering FLC as a valid alternative to OCB for MS diagnosis (Bayart et al., 2018; Puthenparampil et al., 2018). The main aim of the present observational study was to retrospectively compare the diagnostic accuracy of CSF  and  indexes to that of CSF IgG OCB in differentiating MS from other inflammatory and non-inflammatory neurological diseases. Moreover, we also compared the role of

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CSF FLC and OCB in predicting conversion from CIS to MS.

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2- Patients and methods

2.1- CSF and serum sampling. We selected for this study 170 consecutive patients whose CSF and

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serum samples were stored in the Biobank of the Clinical Pathology Laboratory of the S. Maria della

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Misericordia Hospital, Perugia (Italy). CSF and serum samples were collected over a 3-year period (January 2014 – January 2017) via lumbar puncture and venipuncture at the Section of Neurology,

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Department of Medicine, University of Perugia, Perugia (Italy), using the same standard operating

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procedures throughout the study, as recommended (Teunissen et al., 2009). Specifically, lumbar puncture was performed between 8:00 and 10:00 a.m. and CSF was collected in sterile polypropylene

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tubes, centrifuged for 10 min at 2000 × g, divided into 0.5 ml aliquots and immediately frozen at −80 °C, together with serum 0.5 ml aliquots, pending analysis. After lumbar puncture, patients’ demographic and clinical data were collected in an online electronic database. The selected CSF samples came from two groups of patients who were diagnosed, at the time of CSF sampling, as follows: (i) radiologically isolated syndrome (RIS), CIS and MS (MS group, n: 64), and (ii) other neurological diseases (control group, n: 106), including both inflammatory neurological diseases (IND) and non-inflammatory neurological diseases (NIND). For all of the patients, CSF was collected as part of their usual diagnostic work-up. The local Ethics Committee approved the protocol (# 2320/14).

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Journal Pre-proof 2.2- Selection of CSF samples. For the MS group, we selected CSF samples from patients satisfying, at the time of lumbar puncture, the following inclusion criteria: (i) a diagnosis of CIS or MS according to the 2010 revision of the McDonald criteria (Polman et al., 2011), or a diagnosis of RIS according to the criteria by Okuda et al. 2009 (Okuda et al., 2009); (ii) age between 18 and 60 years; (iii) no history of exposure, in the 30 days prior to CSF collection, to immunosuppressant or immunomodulatory therapies. On the contrary, for the control group, we selected patients with a diagnosis of both inflammatory and non-inflammatory neurological diseases, with age between 18 and 60 years, not

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exposed to immunosuppressant therapies in the 30 days preceding lumbar puncture.

2.3- MS patients’ clinical assessment. A senior neurologist with experience in the field of MS

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examined all the study participants and scored the Kurtzke’s Expanded Disability Status Scale (EDSS)

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(Kurtzke, 1983). At the time of lumbar puncture, patients also underwent a 1.5 Tesla brain and spinal

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cord contrast-enhanced magnetic resonance imaging (MRI) as part of their usual diagnostic work-up (Filippi et al., 2013). Patients were followed-up clinically and radiologically according to routine

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clinical practice and, for all of them, the follow-up ended in October 2017. Since, in all cases, CSF

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was collected during the diagnostic assessment, none of the patients was on disease-modifying therapy at the time of lumbar puncture. For patients with a baseline diagnosis of CIS, conversion to MS was

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defined during the follow-up either clinically or radiologically, according to the 2010 revision of the McDonald criteria (Polman et al., 2011).

2.4- CSF and serum analysis: IgG OCB determination. OCB pattern detection was achieved by running both serum and coupled CSF samples by means of IEF (Deisenhammer et al., 2006), on a semi-automated agarose electrophoresis system (Sebia Hydrasys) followed by immunofixation with a peroxidase labeled anti-IgG (Hydragel 9 CSF Isoelectrofocusing; Sebia). An aliquot of each serum sample was appropriately diluted in order to adjust the IgG concentration to the same level as found in the CSF, as specified by the manufacturer. The presence of OCB was evaluated by two independent operators and reported according to the Consensus Report of the Committee of the European Concerted Action for Multiple Sclerosis. Following OCB pattern evaluations, patients were divided

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Journal Pre-proof into two groups: (i) OCB negative (IEF patterns 1, 4, and 5), and (ii) OCB positive (IEF patterns 2 and 3), when ≥ 2 additional OCB were detected in CSF compared to serum sample (Andersson et al., 1994; Freedman et al., 2005).

2.5- CSF and serum analysis: FLC and FLC indexes determination. Serum and CSF albumin and IgG, were assessed by nephelometry in a Siemens TM BN II automated analyzer (Siemens Healthcare Diagnostics) using albumin and IgG immunoassay (Siemens Healthcare Diagnostics) following the

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manufacturer’s protocol.  and FLC concentrations were assessed by nephelometry with the same automated analyzer using free light chain immunoassays Freelite LK016 for FLC, and Freelite

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LK018 for FLC (The Binding Site Ltd). The lower limit of detection was 0.06 mg/L for FLC, and 0.05 mg/L for FLC. The FLC assay is based on affinity purified polyclonal antibodies coated onto

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latex particles that react only with exposed FLC epitopes that are hidden when the light chain is bound

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to the heavy chain. For FLC quantification, serum samples were diluted 1:100, while CSF samples were diluted 1:1 following the manufacturer instructions for use. The CSF/serum albumin ratio (Qalb)

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was calculated to assess BBB damage and changes in permeability. Results obtained were used to

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elaborate  and  index values according to the following formulas, respectively: (i) -index = (CSF

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FLC/serum FLC)/Qalb; (ii) -index = (CSF FLC/serum FLC)/Qalb.

2.6- Statistical analysis. Continuous variables were reported as mean ± standard deviation when normally distributed, and as median (interquartile range - IQR) when non-normally distributed. Categorical variables were reported as absolute number and percentage. Comparison between continuous variables not normally distributed was performed by Mann-Whitney test. Comparison between categorical variables distributions was performed by Chi-squared test. Correlation’s analysis between continuous variables was performed by means of Spearman’s rank correlation test. The diagnostic accuracy of examined markers was evaluated by the Area Under the Curve (AUC) of the Receiver Operator Characteristic (ROC) curve. Reported cut-off values were calculated as the values that maximized the Youden’s index (J = sensitivity + specificity - 1). For CSF OCB, κ-index and -

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Journal Pre-proof index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratio (LR) were calculated for each comparison. Additionally, sensitivity, specificity, PPV, NPV and LR of different -index cut-off values previously tested in literature were calculated. Finally, a survival analysis was performed by means of Cox regression models in order to investigate the role of the tested biomarkers in predicting a faster conversion from CIS to MS. All tests were 2-sided, and significance was set at p < 0.05. Statistical analyses were performed using R software, version 3.5.

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3- Results

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3.1- Patients’ characteristics. The MS group included 64 patients. Among them, 3 patients had a diagnosis of RIS, 23 of CIS, 34 of relapsing-remitting MS (RRMS) and 4 of progressive MS (PMS).

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Within the control group (n: 106 patients), 24 patients were affected by IND, while 82 patients by

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NIND. Details of MS and control groups characteristics are reported in Table 1 and 2. For 19/23 CIS patients (82.6%), data on the follow-up were available. Over a median follow-up of 39.1 (IQR: 27)

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months, 12 CIS patients (63.2%) converted to MS, either clinically or radiologically. Median time to

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conversion was 13.3 (IQR: 21) months, and the time when the 50% of patients had converted to MS was 24 months. Between CIS onset and conversion to MS, none of these patients was started on

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disease-modifying drugs.

3.2- CSF FLC results. The results of FLC measurement are reported in detail in Table 3. All the included patients had measurable concentration of CSF FLC. The following parameters were significantly higher in MS compared to controls: CSF FLC (2 [IQR: 7.1] vs 0.1 [IQR: 0.2] mg/L, p<0.001), CSF FLC (0.4 [IQR: 1.1] vs 0.1 [IQR: 0.1] mg/L, p<0.001), CSF FLC/FLC (3.4 [IQR: 11.8] vs 1.2 [IQR: 0.7] mg/L, p<0.001), FLCratio (0.4 [IQR: 1.3] vs 0.02 [IQR: 0.04] mg/L, p<0.001), FLCratio (0.06 [IQR: 0.2] vs 0.02 [IQR: 0.02] mg/L, p<0.001), -index (65.5 [IQR: 280.5] vs 2.9 [IQR: 5], p<0.001), and -index (11.2 [37.8] vs 2.4 [2.1], p<0.001) (Table 3). Further analysis,

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Journal Pre-proof however, exclusively took into account -index and -index, being them the most informative parameters to evaluate intrathecal FLC synthesis.

3.3- Diagnostic performance of CSF OCB, κ-index and λ-index: MS vs controls. No significant difference was found in the accuracy of CSF OCB, -index and -index in discriminating between MS and controls. Specifically, the diagnostic accuracy of OCB in discriminating between MS and controls was defined by an AUC of 0.87 (95% CI = 0.81-0.93). Sensitivity and specificity were 83% (95% CI

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= 72-90%) and 92% (95% CI = 85-95%), respectively. PPV was 86% (95% CI = 75-92), NPV was 90% (95% CI = 83-94), with a LR of 9.8. -index discriminated MS patients from controls with an

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AUC of 0.91 (95% CI = 0.87-0.96), a sensitivity of 89% (95% CI = 79-95%), a specificity of 81% (95% CI = 73-87%), a PPV of 74% (95% CI = 63-83), a NPV of 93% (95% CI = 85-96) and a LR of

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4.7, referred to a cut-off value of 7.83 (Table 4). AUC for -index was 0.85 (95% CI = 0.79-0.91),

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sensitivity and specificity were 80% (95% CI = 68-88%) and 78% (95% CI = 70-85%), respectively, while PPV was 69% (95% CI = 58-78), NPV was 87% (95% CI = 78-92), and LR was 3.7 when using

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3.87 as a cut-off (Figure 1).

3.4- Frequency of positivity to CSF OCB and FLC indexes: MS vs controls. Within the MS group,

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53 (82.8%) patients were OCB positive, 57 (89.1%) were -index positive (-index ≥ 7.83) and 51 (79.7%) were -index positive (-index ≥ 3.87). Specifically, 20 CIS patients (87%), 30 RRMS patients (88.2%) and 3 PMS patients (75%) were CSF OCB positive, and none of RIS patients was positive to OCB. Positivity to -index and -index was found in 20 (87%) and 17 (73.9%) CIS patients, 32 (94.1%) and 28 (82.4%) RRMS patients, 3 (75%) and 4 (100%) PMS patients, 2 (66.7%) and 2 (66.6%) RIS patients, respectively. Within controls, instead, 9 patients (8.5%) were CSF OCB positive, 20 (18.9%) were -index positive and 23 (21.7%) were -index positive (Table 5).

3.5- Diagnostic performance of CSF OCB, κ-index and λ-index: MS vs NIND. CSF OCB, -index and -index were not statistically different in terms of accuracy in discriminating MS from NIND

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Journal Pre-proof patients. Specifically, diagnostic accuracy of OCB was 0.90 (95% CI = 0.84-0.96), with a sensitivity of 83% (95% CI = 72-90%), a specificity of 96% (95% CI = 90-99%), a PPV of 95% (95% CI = 9099), a NPV of 88% (95% CI = 79-93) and a LR of 22.6. -index also showed an excellent diagnostic accuracy in discriminating MS patients from NIND group. When using the cut-off value of 7.83, AUC was 0.92 (95% CI = 0.87-0.97), with sensitivity and specificity of 89% (95% CI = 79-95%) and 83% (95% CI = 73-90%), respectively, PPV of 80% (95% CI = 70-88), NPV of 91% (95% CI = 82-96) and LR of 5.2. In the same comparison, -index showed a not-significantly worse accuracy (AUC: 0.87

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[95% CI = 0.81-0.93], sensitivity: 80% [95% CI = 68-88%], specificity: 82% [95% CI = 72-89%], PPV: 77% [95% CI = 66-86], NPV: 84% [95% CI = 74-90], LR: 4.4, for a threshold of 3.87) (Figure

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2).

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3.6- Diagnostic performance of CSF OCB, κ-index and λ-index: MS vs IND. -index performed

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significantly better than OCB in discriminating MS from IND patients (p<0.001). The diagnostic accuracy of OCB in this comparison was 0.79 (95% CI = 0.68-0.90), with a sensitivity of 83% (95%

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CI = 72-90%), a specificity of 75% (95% CI = 55-88%), a PPV of 90% (95% CI = 80-95), a NPV of

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62% (95% CI 44-77) and a LR of 3.3. -index showed a high diagnostic accuracy in discriminating

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MS from IND patients, with an AUC of 0.90 (95% CI = 0.83-0.96), a sensitivity of 86% (95% CI = 75-92%), a specificity of 83% (95% CI = 64-93%), a PPV of 93% (95% CI = 84-97), a NPV of 69% (95% CI = 51-83) and a LR of 5.2, referred to 10.6 as the best performing threshold. No significant difference, instead, resulted in comparing OCB to -index. In MS vs IND comparison, -index showed an AUC of 0.78 (95% CI = 0.69-0.88), with a 80% sensitivity (95% CI = 68-88%), a 67% specificity (95% CI = 47-82%), a 86% PPV (95% CI = 76-93), a 55% NPV (95% CI = 38-72), and a 2.4 LR when the cut-off value was set at 3.87 (Figure 3).

3.7- Correlation between FLC indexes and clinical findings within the MS group. When correlating -index and -index to the specific clinical phenotype presented by each patient within the MS group at baseline, individuals suffering from myelitis had higher values of -index than those

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Journal Pre-proof presenting with symptoms related to a brainstem/cerebellar lesion (166.6 [IQR: 299.3] vs 15.4 [IQR: 52.7], p = 0.019) (Figure 4). At the same time, -index resulted to be higher in patients with myelitis than in patients with optic neuritis (32.5 [IQR: 85.1] vs 6.4 [IQR: 20.7], p = 0.030). No significant results were found when we correlated -index and -index values to EDSS scores at the baseline, nor with the number of contrast-enhanced lesions on MRI. On the contrary, increasing values of -index were associated with a higher number of T2 lesions on MRI. Patients with > 9 T2 lesions showed significantly higher levels of -index compared to patients with ≤ 9 T2 lesions (190.6 [IQR: 305.8] vs

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23 [IQR: 106.2], p = 0.033) (Figure 4). Finally, no significant differences emerged in -index and index between RIS, CIS, RRMS and PMS patients. CSF OCB did not predict time to conversion in

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CIS patients. On the contrary, time to conversion to MS was significantly predicted by -index values

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≥ 10.6 (concordance = 0.63, p = 0.020), with the 50% of patients with -index ≥ 10.6 converting in 21

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months (Figure 4). λ-index did not show a similar prognostic effect.

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4- Discussion

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CSF analysis has now re-entered the diagnostic criteria for MS, since the evidence of intrathecal IgG

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synthesis can substitute for dissemination in time (Thompson et al., 2018). The application of these new criteria allows neurologists to anticipate the diagnosis of MS (Gaetani et al., 2018), with significant consequences in the therapeutic management. However, since criteria for dissemination in space have been simplified, it is of high relevance that intrathecal IgG synthesis is demonstrated in the most accurate way, in order to avoid misdiagnosis. FLC may represent a valid alternative to OCB, as the method is less time-consuming and less rater-dependent (Presslauer et al., 2016). In our study, we confirmed that -index might be considered as a potential diagnostic biomarker in MS, since it showed an overall accuracy almost similar to OCB in discriminating between MS and controls (0.91 vs. 0.87, respectively). A more detailed view on the different performances of -index and OCB showed that the former had a higher sensitivity (89% vs. 83%), but a lower specificity (92% vs. 81%) compared to the latter. Indeed, within MS patients, a slightly higher percentage of individuals turned

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Journal Pre-proof out to be -index positive (89.1%) than CSF OCB positive (82.8%). As such, our findings are in line with a recent large multicenter study performed on 745 patients, where a higher sensitivity and a lower specificity of -index compared to OCB for MS diagnosis were found. Of interest, sensitivity and specificity of both -index and OCB turned out to be very close to what we found in our cohort (-index sensitivity: 88%; OCB sensitivity: 82%; -index specificity: 83%; OCB specificity: 92%) (Leurs et al., 2019). The higher sensitivity and the lower specificity of -index compared to OCB, can lead to consider the

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use of -index in the clinical practice as a sort of screening test. Indeed, -index can be easily performed in every hospital setting in the suspicion of MS, since it is less technically demanding.

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Should the -index be positive and the clinical and MRI picture be atypical for MS, then

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isoelectrofocusing could represent a confirmation test, to be carried out in specialized laboratories, as it has already been proposed (Valencia-Vera et al., 2018).

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In our study, we tried to assess the diagnostic performance of -index and OCB in different comparisons between different clinical groups. For instance, when assessing the diagnostic accuracy

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of these two biomarkers in the discrimination between MS and controls affected by non-inflammatory

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neurological diseases (such as cerebral small vessel disease or unspecific white matter lesions, which

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often enter the differential diagnosis of MS), -index performed almost similarly to CSF OCB (overall accuracy 0.92 vs. 0.90), again with a slightly and non-significantly higher sensitivity (89% vs 83%), and lower specificity (83% vs. 96%). On the contrary, FLC showed a significant benefit over OCB in the discrimination between patients with MS and patients with other inflammatory neurological diseases. Indeed, in this specific comparison, -index performed significantly better than OCB with a higher accuracy (0.90 vs. 0.79), almost dependent of a greater specificity (83% vs. 75%). Of note, all the above-mentioned accuracies of -index have been generated after testing the best performing cut-off value in our cohort, which was 7.83 for the comparison between MS and controls. However, in other studies, different cut-offs have been tested. For instance, slightly higher cut-off values were tested by Pieri and colleagues (12.3) (Pieri et al., 2017), and by Gurtner and co-workers (10.5), although with a worse performance in terms of diagnostic accuracy (AUC 0.86, 95% CI =

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Journal Pre-proof 0.83-0.90) (Gurtner et al., 2018). Puthenparampil and colleagues recently found that a -index ≥ 4.25 was highly sensitive and specific in identifying patients with intrathecal IgG synthesis (Puthenparampil et al., 2018), while others have used 5.9 or 6.6 as the upper threshold for normality (Leurs et al., 2019; Presslauer et al., 2016, 2008; Schwenkenbecher et al., 2018). When applying these above-mentioned cut-offs to our study cohort in the comparison between MS and controls, we found, as expected, that lower cut-off values showed higher sensitivity and NPV, while higher cut-offs had higher specificity, PPV and LR (Table 4). However, our study demonstrates that probably, the best

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performing cut-off depends on the specific diagnostic context. For instance, in those cases where the differential diagnosis is between MS and inflammatory neurological diseases, a higher cut-off should

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be preferred.

Another potential context of use of -index would be as an alternative to OCB in substituting for

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dissemination in time in patients with CIS. To this regard, -index should at least have the same

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prognostic value that has been demonstrated for OCB in CIS patients (Tintore et al., 2015). In our study cohort, we had a small group of individuals enrolled at the time of the first demyelinating event

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who had a median follow-up of about 3 years. In this population, we found that -index significantly

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predicted time to conversion to MS, while the same was not true for OCB. The potential prognostic effect of -index in early MS patients has been already investigated. In a previous study that enrolled

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CIS patients defined according to different diagnostic criteria, no difference on -index values between patients who converted to MS and patients who did not was found (Presslauer et al., 2014). However, in a CIS cohort similar to ours, the authors found that patients with -index ≥ 10.62 had a 7.34-fold risk of developing MS during the follow-up, the cut-off being the same we found to predict conversion to MS (Menéndez-Valladares et al., 2015). When interpreting our results, some study limitations have to be considered. For instance, the evaluation of the potential prognostic effect of FLC in CIS was limited by the small number of patients who entered the survival analysis. In addition, CIS was defined according to the 2010 revision of the McDonald criteria (Polman et al., 2011). If the last revision of the McDonald criteria had been applied (Thompson et al., 2018), the large majority of CIS patients would have already been

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Journal Pre-proof diagnosed with MS. However, despite these limitations, our paper has the value of having investigated the diagnostic value of FLC in in discriminating between MS and both NIND and IND patients, and of having confirmed the potential prognostic value in patients at the first demyelinating episode. In conclusion, among FLC parameters, -index is confirmed to be a potential diagnostic biomarker for MS, characterized by a diagnostic accuracy that is similar to that of OCB, although with modest differences in terms of sensitivity and specificity. In general, the higher sensitivity and the lower

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specificity of -index compared to OCB could lead considering -index as a first-line test, to be followed by isoelectrofocusing in cases of positivity. -index has also shown a prognostic value in

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CIS patients, which makes it possible to consider, among potential contexts of use, the possibility of this biomarker to substitute for dissemination in time at the disease onset, as it is now for OCB. Our

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findings improve the present knowledge about -index, specifically highlighting that the diagnostic

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cut-off values of this biomarker depend on the clinical context of use. Additionally, -index in the earliest phases of MS might have a prognostic value, which requires confirmation through studies on

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multi-center cohorts, with centralized laboratory analysis.

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Journal Pre-proof Figure legends. Figure 1. Diagnostic performance of κ-index, λ-index, and OCB in MS and control patients. (A) Scatter plot showing the distribution of κ-index in MS and control patients. The dotted line corresponds to the identified cut-off value of 7.83. (B) Scatter plot showing the distribution of λ-index in MS and control patients. The dotted line corresponds to the identified cut-off value of 3.87. (C) ROC curves of κ-index, λ-index, and OCB for the discrimination between MS and controls. Legend. AUC, area under the curve. CI, confidence interval. MS, multiple sclerosis. OCB, oligoclonal bands. ROC, receiver operating characteristics.

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Figure 2. Diagnostic performance of κ-index, λ-index, and OCB in MS and NIND patients. (A) Scatter plot showing the distribution of κ-index in MS and NIND patients. The dotted line corresponds to the identified cut-off value of 7.83. (B) Scatter plot showing the distribution of λ-index in MS and

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NIND patients. The dotted line corresponds to the identified cut-off value of 3.87. (C) ROC curves of κ-index, λ-index, and OCB for the discrimination between MS and NIND. Legend. AUC, area under

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the curve. CI, confidence interval. MS, multiple sclerosis. NIND, non-inflammatory neurological

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diseases. OCB, oligoclonal bands. ROC, receiver operating characteristics. Figure 3. Diagnostic performance of κ-index, λ-index, and OCB in MS and IND patients. (A) Scatter

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plot showing the distribution of κ-index in MS and IND patients. The dotted line corresponds to the identified cut-off value of 10.61. (B) Scatter plot showing the distribution of λ-index in MS and IND

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patients. The dotted line corresponds to the identified cut-off value of 3.87. (C) ROC curves of κ-

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index, λ-index, and OCB for the discrimination between MS and IND. Legend. AUC, area under the curve. CI, confidence interval. IND, inflammatory neurological diseases. MS, multiple sclerosis. OCB, oligoclonal bands. ROC, receiver operating characteristics. Figure 4. Association between κ-index and disease characteristics in MS patients. (A) Scatter plots showing the distribution of κ-index according to the type of relapse occurring at the time of CSF sampling. Median and 95% CI are depicted. (B) Scatter plots showing the distribution of κ-index according to the number of brain MRI T2 lesions. Median and 95% CI are depicted. (C) Survival curve reporting the proportion of patients at the first demyelinating event with κ-index ≥ 10.6 and < 10.6 who did not convert to multiple sclerosis during the follow-up. Legend. Cb, cerebellar. CI, confidence interval. CSF, cerebrospinal fluid. MRI, magnetic resonance imaging. MS, multiple sclerosis.

13

Journal Pre-proof Tables

Table 1. Demographic, clinical, and radiological characteristics at baseline of patients within the MS group.

Total

RIS

CIS

RRMS

PMS

64

3 (4.7%)

23 (35.9%)

34 (53.1%)

4 (6.3%)

S ex F, n (%)

48 (75%)

1 (33.3%)

18 (78.3%)

27 (79.4%)

2 (50%)

Age, mean ± S D

40 ± 12.3

41 ± 4.6

41.8 ± 10.8

37 ± 12.4

53 ± 15.4

Hemispheric syndrome

7 (10.9%)

-

2 (8.7%)

5 (14.7%)

-

M yelitis

25 (39.1%)

-

9 (39.1%)

16 (47.1%)

-

Brainstem/cerebellar syndrome

10 (15.6%)

-

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MS group

4 (17.4%)

6 (17.6%)

-

Optic neuritis

14 (21.9%)

-

8 (37.8%)

6 (17.6%)

-

EDS S , mean ± S D

2±1

1.3 ± 0.6

1.7 ± 0.6

1.9 ± 0.8

3.8 ± 1.9

Patients, n (%)

e-

Pr

MRI features, n (%)

pr

Relapse at the time of LP, n (%)

8 (12.5%)

1 (33.3%)

6 (26.1%)

3 (8.8%)

0

4-9 T2 lesions

23 (35.9%)

2 (66.7%)

11 (47.8%)

7 (20.6%)

3 (75%)

>9 T2 lesions

31 (48.4%)

0

6 (26.1%)

24 (70.6%)

1 (25%)

35 (54.7%)

0

12 (52.2%)

23 (67.6%)

0

rn

Gd+ lesions

al

1-3 T2 lesions

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Legend. CIS : clinically isolated syndrome. EDS S : Expanded Disability Status Scale. Gd+: gadolinium-enhancing lesions. LP: lumbar puncture. MRI: magnetic resonance imaging. MS : multiple sclerosis. PMS : progressive multiple sclerosis. RIS : radiologically isolated syndrome. RRMS : relapsing-remitting multiple sclerosis.

14

Journal Pre-proof Table 2. Demographic and clinical characteristics at baseline of patients within the control group.

Total

NIND

IND

106

82 (77.4%)

24 (22.6%)

S ex F, n (%)

62 (58.5%)

49 (59.8%)

13 (54.2%)

Age, mean ± S D

55.2 ± 18.4

53.6 ± 19.9

58.4 ± 13.4

28 (26.4%)

28 (34.1%)

-

White matter lesions due to CSVD

23 (21.7%)

23 (28%)

-

Non inflammatory neuropathies

11 (10.4%)

11 (13.4%)

-

Inflammatory neuropathies

9 (8.5%)

-

9 (37.5%)

Autoimmune encephalitis

7 (7.5%)

-

7 (29.2%)

6 (5.7%)

-

6 (25%)

5 (4.7%)

5 (6.1%)

-

e-

Control group

5 (4.7%)

5 (6.1%)

-

4 (3.8%)

4 (4.93%)

-

3 (2.8%)

3 (3.7%)

-

3 (2.8%)

3 (3.7%)

-

2 (1.9%)

-

2 (8.3%)

Patients, n (%)

Most frequent diagnoses, n (%) Neurodegenerative diseases

pr

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(including AD, FTD, PD, APS, ALS)

Infectious encephalitis and meningoencephalitis

Epilepsy Psychiatric disorders Toxic or metabolic encephalopathies Neuro-oncologic diseases Paraneoplastic myelitis

Pr

Headache

al

Legend. AD: Alzheimer’s disease. ALS : amyotrophic lateral sclerosis. APS : atypical parkinsonian syndromes. CS VD: cerebral small vessel disease. FTD: frontotemporal dementia. IND: inflammatory neurological diseases. NIND: non-

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rn

inflammatory neurological diseases. PD: Parkinson’s disease.

15

Journal Pre-proof Table 3. FLC determinations in MS and control group. Values are shown as median (IQR). Statistical significance of the comparison between MS group and control group is reported. MS group

Difference

Control group

Total

RIS

CIS

RRMS

PMS

Total

NIND

IND

p-value

7.2 (7.8)

5.1 *

8.5 (7.6)

5.3 (7.1)

9.6 (58.6)

6.5 (6)

6.9 (5.7)

5.4 (6.8)

0.137

8 (7)

4.6 *

9.5 (8.7)

7.3 (6.2)

6.9 (6.8)

6 (5.4)

6.7 (5.7)

5.2 (3.7)

0.722

2 (7.1)

0.2 *

2.2 (8.2)

3.1 (7.6)

2.2 (5.9)

0.1 (0.2)

0.1 (0.1)

0.2 (0.3)

< 0.001

0.4 (1.1)

0.1 *

0.2 (0.7)

0.6 (1.6)

0.6 (3.9)

0.1 (0.1)

0.1 (0.1)

0.1 (0.2)

< 0.001

0.9 (0.4)

1.2 *

0.9 (0.5)

0.9 (0.4)

1.6 (5)

1 (0.5)

1 (0.5)

1 (0.7)

0.769

3.4 (11.8)

1.7 *

7 (22.2)

3.2 (6.6)

1.8 (39.1)

1.2 (0.7)

1.2 (0.6)

1.2 (0.8)

< 0.001

κ-FLC ratio

0.4 (1.3)

0.05 *

0.5 (1.2)

0.5 (1.8)

0.3 (0.7)

0.02 (0.04)

0.02 (0.02)

0.04 (0.03)

< 0.001

λ-FLC ratio

0.06 (0.2)

0.02 *

0.04 (0.1)

0.1 (0.3)

0.1 (0.4)

0.02 (0.02)

0.02 (0.01)

0.03 (0.04)

< 0.001

κ-index

65.5 (280.5)

7.9 *

64.6 (282.6)

77.2 (297.8)

61.8 (138.8)

2.9 (5)

2.7 (3.6)

4.8 (6.6)

< 0.001

λ-index

11.2 (37.8)

6.4 *

6.4 (16.2)

24.6 (56.4)

2.4 (2.1)

2.1 (2.2)

3.1 (3.4)

< 0.001

S erum κ-FLC (mg/L) S erum λ-FLC (mg/L) CS F κ-FLC (mg/L)

(mg/L)

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CS F λ-FLC S erum κ-FLC / λ-FLC (mg/L)

e-

Pr

19.5 (79)

Legend. CIS : clinically isolated syndrome. IND: inflammatory neurological diseases. MS : multiple sclerosis. NIND: non-

al

inflammatory neurological diseases. PMS : progressive multiple sclerosis. RIS : radiologically isolated syndrome. RRMS :

rn

relapsing-remitting multiple sclerosis. * IQR values are missing due to the small number of subjects in the RIS subgroup (n: 3).

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FLC (mg/L)

pr

CS F κ-FLC / λ-

16

Journal Pre-proof Table 4. Sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratio of different cut-off values of κ-index and of CSF OCB in the discrimination between MS patients and controls within our study cohort. MS vs controls S ensitivity

S pecificity

PPV

NPV

Likelihood

(95% CI)

(95% CI)

(95% CI)

(95% CI)

(95% CI)

ratio

κ-index ≥ 4.25 *

0.77 (0.70-0.84)

92 (83-97)

63 (54-72)

60 (50-69)

93 (85 -97)

2.5

κ-index ≥ 5.9 **

0.81 (0.74-0.87)

89 (79-95)

74 (65-81)

67 (57-76)

92 (84-96)

3.4

κ-index ≥ 6.6 §

0.82 (0.75-0.88)

89 (79-95)

75 (66-82)

68 (57-77)

92 (84-96)

3.5

κ-index ≥ 7.83

0.91 (0.87-0.96)

89 (79-95)

81 (73-87)

74 (63-83)

93 (85-96)

4.7

κ-index ≥ 10.5 §§

0.83 (0.77-0.90)

86 (75-92)

84 (76-90)

76 (65-85)

91 (85-95)

5.4

κ-index ≥ 12.3 ¶

0.82 (0.75-0.89)

80 (68-88)

85 (77-91)

76 (65-85)

87 (80-93)

5.3

CS F IgG OCB ≥ 2

0.87 (0.81-0.93)

83 (72-90)

92 (85-96)

90 (83-94)

9.8

pr

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AUC

86 (75-92)

Legend. Values in bold refer to the κ-index cut-off that maximized Youden’s index in our study. All the other κ-index cut-

e-

offs were tested in other studies. * (Puthenparampil et al., 2018). ** (Presslauer et al., 2016, 2008; Schwenkenbecher et al., 2018). § (Leurs et al., 2019). §§ (Gurtner et al., 2018). ¶ (Pieri et al., 2017). AUC: area under the receiver operating

Pr

characteristic curve. CI: confidence interval. CS F: cerebrospinal fluid. MS : multiple sclerosis. NPV: negative predictive value. OCB: oligoclonal bands. PPV: positive predictive value.

al

Table 5. Distribution of positivity to OCB, κ-index and λ-index in MS and controls.

rn

MS group

Control group Total

NIND

IND

64

106

82

24

CS F OCB ≥ 2 (n; %)

53 (82.8%)

9 (8.5%)

3 (3.7%)

6 (25%)

κ-index ≥ 7.83 (n; %)

57 (89.1%)

20 (18.9%)

14 (17.1%)

6 (25%)

κ-index ≥ 10.61 (n; %)

55 (85.9%)

17 (16%)

13 (15.9%)

4 (16.7%)

λ-index ≥ 3.87 (n; %)

51 (79.7%)

23 (21.7%)

15 (18.3%)

8 (33.3%)

N

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Total

Legend. IND: inflammatory neurological diseases. MS : multiple sclerosis. NIND: non-inflammatory neurological diseases. OCB: oligoclonal bands.

17

Journal Pre-proof

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Competing interests’ statement

LGa participated on advisory boards for, and received writing honoraria and travel grants from Almirall, Biogen, Merck, Mylan, Novartis, Roche, Sanofi Genzyme and Teva. AM received travel grants from Biogen, Biogen-Idec, Novartis, Teva and Sanofi Genzyme. PC received/receive

research

support

from

Bayer

Schering,

Biogen-Dompé,

Boehringer

Ingelheim, Eisai, Lundbeck, Merck-Serono, Novartis, Sanofi-Aventis, Sigma-Tau, and UCB Pharma. MDF participated on advisory boards for and received speaker or writing honoraria 21

Journal Pre-proof and funding for travelling from Bayer, Biogen Idec, Genzyme, Merck, Novartis, Roche and Teva. MDC, GB, FV, PE, LGe, AB, PS, CF and AV report no competing interests. Highlights

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 

-index is more sensitive and less specific than OCB for MS diagnosis -index performs better than OCB in discriminating MS from other inflammatory neurological diseases -index correlates with different disease characteristics in MS -index predicts disease activity in early MS patients

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 

22

Figure 1

Figure 2

Figure 3

Figure 4