The discriminating variable (DIVA) test and the selectivity ratio (SR) plot are developed as quantitative tools for revealing the variables in spectral or chromatographic profiles discriminating best between two groups of samples. The SR plot is visually similar to a spectrum or a chromatogram, but with the most intense regions corresponding to the most discriminating variables. Thus, the variables with highest SR represent the variables most important for interpretation of differences between groups. Regions with variables that are positively or negatively correlated to each other are displayed as corresponding negative and positive regions in the SR plot. The nonparametric DIVA test is designed for connecting SR to discriminatory ability of a variable quantified as probability for correct classification. A mean probability for a certain SR range is calculated as the mean correct classification rate (MCCR) for all variables in the same SR interval. The MCCR is thus similar to a mean sensitivity in each SR interval. In addition to the ranking of all variables according to their discriminatory ability provided by the SR plot, the DIVA test connects a probability measure to each SR interval. Thus, the DIVA test makes it possible to objectively define thresholds corresponding to mean probability levels in the SR plot and provides a quantitative means to select discriminating variables. In order to validate the approach, samples of untreated cerebrospinal fluid (CSF) and samples spiked with a multicomponent peptide standard were analyzed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry. The differences in the multivariate spectral profiles of the two groups were revealed using partial least-squares discriminant analysis (PLS-DA) followed by target projection (TP). The most discriminating mass-to-charge (m/z) regions were revealed by calculating the ratio of explained to unexplained variance for each m/z number on the target-projected component and displaying this measure in SR plots with quantitative boundaries determined from the DIVA test. The results are compared to some established methods for variable selection.

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.

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In the present study, we aimed to discover and verify proteins with differential abundance in cerebrospinal fluid (CSF) from patients with early multiple sclerosis compared to controls. iTRAQ and Orbitrap MS was used to compare the CSF proteome of patients with clinically isolated syndrome (CIS) (n = 5), patients with relapsing-remitting multiple sclerosis that had CIS at the time of lumbar puncture (n = 5), and controls with other inflammatory neurological disease (n = 5). Of more than 1200 identified proteins, five were selected as biomarker candidates. Selected reaction monitoring (SRM) was used to verify the biomarker candidates in a larger patient and control cohort (n = 132). We also included proteins reported as differentially abundant in multiple sclerosis in the literature for SRM verification. We found differential abundance of 11 proteins after verification, of which the five proteins alpha-1-antichymotrypsin, contactin-1, apolipoprotein D, clusterin, and kallikrein-6 show potential as diagnostic markers for multiple sclerosis. This study forms the basis for further biomarker verification studies in even larger sample cohorts, to determine if these proteins have clinical relevance as biomarkers for multiple sclerosis.