Integrative biochemical, proteomics and metabolomics cerebrospinal fluid biomarkers predict clinical conversion to multiple sclerosis

Abstract

Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalised medicine approach. We sought to identify biomarkers from biochemical, metabolic, and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2–10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism, and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis.

Determination of CSF GFAP, CCN5, and vWF Levels Enhances the Diagnostic Accuracy of Clinically Defined MS From Non-MS Patients With CSF Oligoclonal Bands

“…Samples were centrifuged at 3,000 x g for 5 minutes before transferring to a 5-mm NMR tube. NMR spectra were acquired at 310 K using a 700-MHz Bruker AVIII spectrometer operating at 16.4 T equipped with a 1 H [ 13 C/ 15 N] TCI cryoprobe (Department of Chemistry, University of Oxford) and processed as previously described (16).…”

“…NMR metabolite measures were converted to absolute concentrations using the internal reference standard (1 mM maleic acid) as previously described (16). To validate the quantification of the metabolites by NMR, the glucose and lactate levels in all CSF samples were measured using a Cobas ® 8000 modular analyser (Roche Diagnostics, Switzerland) coupled with the Gluc3 and LAC2 assays, respectively.…”

Depressive symptoms in non-alcoholic fatty liver disease are identified by perturbed lipid and lipoprotein metabolism

“…Multivariate OPLS-DA was performed on clinical and metabolome data using in-house R scripts and the ropls package [ 10 ]. Models were computed with matched class sizes and validated on independent test sets using a 10-fold external cross-validation strategy with 100 repetitions ( S1 Fig ), as previously described [ 11 ]. The accuracies of OPLS-DA models were compared to a null distribution (by randomising sample/group assignments) using the two-sided Kolmogorov-Smirnov test.…”

Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

“…Clinical metabolomics is the comprehensive investigation of low molecular weight (< 1500 Da) metabolites within a biological sample and is emerging as a useful downstream ‘omics’ platform for biomarker discovery and in the understanding of disease pathophysiology. 12 Our previous work on metabolomics in neuroinflammatory diseases showed that metabolites can function as diagnostic and prognostic biomarkers by: (i) distinguishing multiple sclerosis from aquaporin-4-antibody disease and myelin oligodendrocyte glycoprotein-antibody disease accurately 13 ; (ii) aiding in the classification of patients with overlapping features of multiple sclerosis and neuromyelitis optica spectrum disorder 14 ; (iii) predicting clinical conversion of clinically isolated syndrome to multiple sclerosis 15 ; and (iv) distinguishing relapsing-remitting multiple sclerosis (RRMS) from secondary progressive multiple sclerosis with high accuracy. 16 , 17 With regards to the application of metabolomics in the context of multiple sclerosis relapses, two studies have reported metabolic perturbations in CSF during relapses compared to remission.…”