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

BackgroundInclusion of cerebrospinal fluid (CSF) oligoclonal IgG bands (OCGB) in the revised McDonald criteria increases the sensitivity of diagnosis when dissemination in time (DIT) cannot be proven. While OCGB negative patients are unlikely to develop clinically definite (CD) MS, OCGB positivity may lead to an erroneous diagnosis in conditions that present similarly, such as neuromyelitis optica spectrum disorders (NMOSD) or neurosarcoidosis.ObjectiveTo identify specific, OCGB-complementary, biomarkers to improve diagnostic accuracy in OCGB positive patients.MethodsWe analysed the CSF metabolome and proteome of CDMS (n=41) and confirmed non-MS patients (n=64) comprising a range of CNS conditions routinely encountered in neurology clinics.ResultsOCGB discriminated between CDMS and non-MS with high sensitivity (85%), but low specificity (67%), as previously described. Machine learning methods revealed CCN5 levels provide greater accuracy, sensitivity, and specificity than OCGB (79%, +5%; 90%, +5%; and 72%, +5% respectively) while glial fibrillary acidic protein (GFAP) identified CDMS with 100% specificity (+33%). A multiomics approach improved accuracy further to 90% (+16%).ConclusionThe measurement of a few additional CSF biomarkers could be used to complement OCGB and improve the specificity of MS diagnosis when clinical and radiological evidence of DIT is absent

A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

The transition from relapsing–remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a ‘real-life’ clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze–thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze–thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze–thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics

Foveal changes in aquaporin‐4 antibody seropositive neuromyelitis optica spectrum disorder are independent of optic neuritis and not overtly progressive

Background and purpose: Foveal changes were reported in aquaporin-4 antibody (AQP4-Ab) seropositive neuromyelitis optica spectrum disorder (NMOSD) patients; however, it is unclear whether they are independent of optic neuritis (ON), stem from subclinical ON or crossover from ON in fellow eyes. Fovea morphometry and a statistical classification approach were used to investigate if foveal changes in NMOSD are independent of ON and progressive. Methods: This was a retrospective longitudinal study of 27 AQP4-IgG + NMOSD patients (49 eyes; 15 ON eyes and 34 eyes without a history of ON [NON eyes]), follow-up median (first and third quartile) 2.32 (1.33-3.28), and 38 healthy controls (HCs) (76 eyes), follow-up median (first and third quartile) 1.95 (1.83-2.54). The peripapillary retinal nerve fibre layer thickness and the volume of combined ganglion cell and inner plexiform layer as measures of neuroaxonal damage from ON were determined by optical coherence tomography. Nineteen foveal morphometry parameters were extracted from macular optical coherence tomography volume scans. Data were analysed using orthogonal partial least squares discriminant analysis and linear mixed effects models. Results: At baseline, foveal shape was significantly altered in ON eyes and NON eyes compared to HCs. Discriminatory analysis showed 81% accuracy distinguishing ON vs. HCs and 68% accuracy in NON vs. HCs. NON eyes were distinguished from HCs by foveal shape parameters indicating widening. Orthogonal partial least squares discriminant analysis discriminated ON vs. NON with 76% accuracy. In a follow-up of 2.4 (20.85) years, no significant time-dependent foveal changes were found. Conclusion: The parafoveal area is altered in AQP4-Ab seropositive NMOSD patients suggesting independent neuroaxonal damage from subclinical ON. Longer follow-ups are needed to confirm the stability of the parafoveal structure over time

Elucidating distinct clinico-radiologic signatures in the borderland between neuromyelitis optica and multiple sclerosis

Background Separating antibody-negative neuromyelitis optica spectrum disorders (NMOSD) from multiple sclerosis (MS) in borderline cases is extremely challenging due to lack of biomarkers. Elucidating different pathologies within the likely heterogenous antibody-negative NMOSD/MS overlap syndrome is, therefore, a major unmet need which would help avoid disability from inappropriate treatment. Objective In this study we aimed to identify distinct subgroups within the antibody-negative NMOSD/MS overlap syndrome. Methods Twenty-five relapsing antibody-negative patients with NMOSD features underwent a prospective brain and spinal cord MRI. Subgroups were identified by an unsupervised algorithm based on pre-selected NMOSD/MS discriminators. Results Four subgroups were identified. Patients from Group 1 termed "MS-like" (n = 6) often had central vein sign and cortical lesions (83% and 67%, respectively). All patients from Group 2 ("spinal MS-like", 8) had short-segment myelitis and no MS-like brain lesions. Group 3 ("classic NMO-like", 6) had high percentage of bilateral optic neuritis and longitudinally extensive transverse myelitis (LETM, 80% and 60%, respectively) and normal brain appearance (100%). Group 4 ("NMO-like with brain involvement", 5) typically had a history of NMOSD-like brain lesions and LETM. When compared with other groups, Group 4 had significantly decreased fractional anisotropy in non-lesioned tracts (0.46 vs. 0.49, p = 0.003) and decreased thalamus volume (0.84 vs. 0.98, p = 0.04). Conclusions NMOSD/MS cohort contains distinct subgroups likely corresponding to different pathologies and requiring tailored treatment. We propose that non-conventional MRI might help optimise diagnosis in these challenging patients

The serum metabolomic profile of a distinct, inflammatory subtype of acute psychosis.

Acknowledgements: This study was funded by NIHR Oxford Health Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.Funder: NIHR Oxford Health Biomedical Research CentreA range of studies suggest that a proportion of psychosis may have an autoimmune basis, but this has not translated through into clinical practice-there is no biochemical test able to accurately identify psychosis resulting from an underlying inflammatory cause. Such a test would be an important step towards identifying who might require different treatments and have the potential to improve outcomes for patients. To identify novel subgroups within patients with acute psychosis we measured the serum nuclear magnetic resonance (NMR) metabolite profiles of 75 patients who had identified antibodies (anti-glycine receptor [GlyR], voltage-gated potassium channel [VGKC], Contactin-associated protein-like 2 [CASPR2], leucine-rich glioma inactivated 1 [LGI1], N-methyl-D-aspartate receptor [NMDAR] antibody) and 70 antibody negative patients matched for age, gender, and ethnicity. Clinical symptoms were assessed using the positive and negative syndrome scale (PANSS). Unsupervised principal component analysis identified two distinct biochemical signatures within the cohort. Orthogonal partial least squared discriminatory analysis revealed that the serum metabolomes of NMDAR, LGI1, and CASPR2 antibody psychosis patients were indistinct from the antibody negative control group while VGKC and GlyR antibody patients had significantly decreased lipoprotein fatty acids and increased amino acid concentrations. Furthermore, these patients had more severe presentation with higher PANSS scores than either the antibody negative controls or the NMDAR, LGI1, and CASPR2 antibody groups. These results suggest that a proportion of patients with acute psychosis have a distinct clinical and biochemical phenotype that may indicate an inflammatory subtype

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

Peer reviewed: TrueBACKGROUND: Inclusion of cerebrospinal fluid (CSF) oligoclonal IgG bands (OCGB) in the revised McDonald criteria increases the sensitivity of diagnosis when dissemination in time (DIT) cannot be proven. While OCGB negative patients are unlikely to develop clinically definite (CD) MS, OCGB positivity may lead to an erroneous diagnosis in conditions that present similarly, such as neuromyelitis optica spectrum disorders (NMOSD) or neurosarcoidosis. OBJECTIVE: To identify specific, OCGB-complementary, biomarkers to improve diagnostic accuracy in OCGB positive patients. METHODS: We analysed the CSF metabolome and proteome of CDMS (n=41) and confirmed non-MS patients (n=64) comprising a range of CNS conditions routinely encountered in neurology clinics. RESULTS: OCGB discriminated between CDMS and non-MS with high sensitivity (85%), but low specificity (67%), as previously described. Machine learning methods revealed CCN5 levels provide greater accuracy, sensitivity, and specificity than OCGB (79%, +5%; 90%, +5%; and 72%, +5% respectively) while glial fibrillary acidic protein (GFAP) identified CDMS with 100% specificity (+33%). A multiomics approach improved accuracy further to 90% (+16%). CONCLUSION: The measurement of a few additional CSF biomarkers could be used to complement OCGB and improve the specificity of MS diagnosis when clinical and radiological evidence of DIT is absent

Corrigendum: Determination of CSF GFAP, CCN5, and vWF levels enhances the diagnostic accuracy of clinically defined MS from non-MS patients with CSF oligoclonal bands

Peer reviewed: Tru

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

Accurate determination of relapses in multiple sclerosis is important for diagnosis, classification of clinical course and therapeutic decision making. The identification of biofluid markers for multiple sclerosis relapses would add to our current diagnostic armamentarium and increase our understanding of the biology underlying the clinical expression of inflammation in multiple sclerosis. However, there is presently no biofluid marker capable of objectively determining multiple sclerosis relapses although some, in particular neurofilament-light chain, have shown promise. In this study, we sought to determine if metabolic perturbations are present during multiple sclerosis relapses, and, if so, identify candidate metabolite biomarkers and evaluate their discriminatory abilities at both group and individual levels, in comparison with neurofilament-light chain. High-resolution global and targeted 1H nuclear magnetic resonance metabolomics as well as neurofilament-light chain measurements were performed on the serum in four groups of relapsing-remitting multiple sclerosis patients, stratified by time since relapse onset: (i) in relapse (R); (ii) last relapse (LR) ≥ 1 month (M) to < 6 M ago; (iii) LR ≥ 6 M to < 24 M ago; and (iv) LR ≥ 24 M ago. Two hundred and one relapsing-remitting multiple sclerosis patients were recruited: R (n = 38), LR 1–6 M (n = 28), LR 6–24 M (n = 34), LR ≥ 24 M (n = 101). Using supervised multivariate analysis, we found that the global metabolomics profile of R patients was significantly perturbed compared to LR ≥ 24 M patients. Identified discriminatory metabolites were then quantified using targeted metabolomics. Lysine and asparagine (higher in R), as well as, isoleucine and leucine (lower in R), were shortlisted as potential metabolite biomarkers. ANOVA of these metabolites revealed significant differences across the four patient groups, with a clear trend with time since relapse onset. Multivariable receiver operating characteristics analysis of these four metabolites in discriminating R versus LR ≥ 24 M showed an area under the curve of 0.758, while the area under the curve for serum neurofilament-light chain was 0.575. Within individual patients with paired relapse–remission samples, all four metabolites were significantly different in relapse versus remission, with the direction of change consistent with that observed at group level, while neurofilament-light chain was not discriminatory. The perturbations in the identified metabolites point towards energy deficiency and immune activation in multiple sclerosis relapses, and the measurement of these metabolites, either singly or in combination, are useful as biomarkers to differentiate relapse from remission at both group and individual levels