In most patients with multiple sclerosis, the disease initiates with a first attack or clinically isolated syndrome. At this phase, magnetic resonance imaging is an important predictor of conversion to multiple sclerosis. With the exception of oligoclonal bands, the role of other biomarkers in patients with clinically isolated syndrome is controversial. In the present study, we aimed to identify proteins associated with conversion to multiple sclerosis in patients with clinically isolated syndrome. We applied a mass spectrometry-based proteomic approach (isobaric labelling) to previously collected pooled cerebrospinal fluid samples from patients with clinically isolated syndrome, who subsequently converted to clinically definite multiple sclerosis (n=30) and patients who remained as having clinically isolated syndrome (n=30). Next, three of the most represented differentially expressed proteins, i.e. ceruloplasmin, vitamin D-binding protein and chitinase 3-like 1 were selected for validation in individual cerebrospinal fluid samples by enzyme-linked immunosorbent assay. Only chitinase 3-like 1 was validated and cerebrospinal fluid levels were increased in patients who converted to clinically definite multiple sclerosis compared with patients who continued as clinically isolated syndrome (P=0.00002) and controls (P=0.012). High cerebrospinal fluid levels of chitinase 3-like 1 significantly correlated with the number of gadolinium enhancing lesions and the number of T2 lesions observed in brain magnetic resonance imaging scans performed at baseline, and were associated with disability progression during follow-up and shorter time to clinically definite multiple sclerosis (log-rank P-value=0.003). Cerebrospinal fluid chitinase 3-like 1 levels were also measured in a second validation clinically isolated syndrome cohort and found to be increased in patients who converted to multiple sclerosis compared with patients who remained as having clinically isolated syndrome (P=0.018). Our results indicate that patients who will convert to clinically definite multiple sclerosis could be distinguished from those patients who will remain as clinically isolated syndrome by proteomic analysis of cerebrospinal fluid samples. Although protein levels are also increased in other disorders characterized by chronic inflammation, chitinase 3-like 1 may serve as a prognostic biomarker for conversion to multiple sclerosis and development of disability which may help to improve the understanding of the aetiopathogenesis in the early stages of multiple sclerosis.

We validated MRI lesion load, OCB and age at CIS as the strongest independent predictors of conversion to CDMS in this multicentre setting. A role for vitamin D is suggested but requires further investigation.

Chitinase 3-like 1 (CHI3L1) has been proposed as a biomarker associated with the conversion to clinically definite multiple sclerosis in patients with clinically isolated syndromes, based on the finding of increased cerebrospinal fluid CHI3L1 levels in clinically isolated syndrome patients who later converted to multiple sclerosis compared to those who remained as clinically isolated syndrome. Here, we aimed to validate CHI3L1 as a prognostic biomarker in a large cohort of patients with clinically isolated syndrome. This is a longitudinal cohort study of clinically isolated syndrome patients with clinical, magnetic resonance imaging, and cerebrospinal fluid data prospectively acquired. A total of 813 cerebrospinal fluid samples from patients with clinically isolated syndrome were recruited from 15 European multiple sclerosis centres. Cerebrospinal fluid CHI3L1 levels were measured by enzyme-linked immunosorbent assay. Multivariable Cox regression models were used to investigate the association between cerebrospinal fluid CHI3L1 levels and time to conversion to multiple sclerosis and time to reach Expanded Disability Status Scale 3.0. CHI3L1 levels were higher in patients who converted to clinically definite multiple sclerosis compared to patients who continued as clinically isolated syndrome (P = 8.1 × 10(-11)). In the Cox regression analysis, CHI3L1 levels were a risk factor for conversion to multiple sclerosis (hazard ratio = 1.7; P = 1.1 × 10(-5) using Poser criteria; hazard ratio = 1.6; P = 3.7 × 10(-6) for McDonald criteria) independent of other covariates such as brain magnetic resonance imaging abnormalities and presence of cerebrospinal fluid oligoclonal bands, and were the only significant independent risk factor associated with the development of disability (hazard ratio = 3.8; P = 2.5 × 10(-8)). High CHI3L1 levels were associated with shorter time to multiple sclerosis (P = 3.2 × 10(-9) using Poser criteria; P = 5.6 × 10(-11) for McDonald criteria) and more rapid development of disability (P = 1.8 × 10(-10)). These findings validate cerebrospinal fluid CHI3L1 as a biomarker associated with the conversion to multiple sclerosis and development of disability and reinforce the prognostic role of CHI3L1 in patients with clinically isolated syndrome. We propose that determining cerebrospinal fluid chitinase 3-like 1 levels at the time of a clinically isolated syndrome event will help identify those patients with worse disease prognosis.

The choice of appropriate control group(s) is critical in cerebrospinal fluid (CSF) biomarker research in multiple sclerosis (MS). There is a lack of definitions and nomenclature of different control groups and a rationalized application of different control groups. We here propose consensus definitions and nomenclature for the following groups: healthy controls (HCs), spinal anesthesia subjects (SASs), inflammatory neurological disease controls (INDCs), peripheral inflammatory neurological disease controls (PINDCs), non-inflammatory neurological controls (NINDCs), symptomatic controls (SCs). Furthermore, we discuss the application of these control groups in specific study designs, such as for diagnostic biomarker studies, prognostic biomarker studies and therapeutic response studies. Application of these uniform definitions will lead to better comparability of biomarker studies and optimal use of available resources. This will lead to improved quality of CSF biomarker research in MS and related disorders.