Derrick Blackmore1, Zaeem Siddiqi2, Liang Li3, Nan Wang3, Walter Maksymowych4. 1. Division of Neurology, University of Alberta, 7th floor, Clinical Sciences Building, 11350 - 83 Ave NW, Edmonton, AB, T6G 2G, Canada. dblackmo@ualberta.ca. 2. Division of Neurology, University of Alberta, 7th floor, Clinical Sciences Building, 11350 - 83 Ave NW, Edmonton, AB, T6G 2G, Canada. 3. Department of Chemistry, University of Alberta, Chemistry Centre Room W3-39C, Edmonton, AB, T6G 2G2, Canada. 4. 568A Heritage Medical Research Centre, University of Alberta, Edmonton, AB, T6G 2S2, Canada.
Abstract
INTRODUCTION:Myasthenia gravis (MG) is a chronic, potentially debilitating autoimmune disease characterized by weakness and rapid fatigue of the voluntary muscles that worsens on exertion. Left untreated, MG symptoms may cause significant morbidity or even death. To date, no robust biological marker is available to follow the course of the disease. Therefore, new diagnostic approaches and biological markers are essential not only for improved diagnosis of the disease but for improved outcomes. OBJECTIVES: The present study applied a two-control, multi-label metabolomics profiling approach as a potential strategy for the identification of biomarkers unique to myasthenia gravis (MG). METHODS: Metabolic analyses using acid- and dansyl-labelled serum from seropositive MG (n = 46), rheumatoid arthritis (RA) (n = 23) and healthy controls (HC) (n = 49) were performed on samples from adult patients presenting to the University of Alberta Hospital neuromuscular and rheumatology clinics. Comparisons between patients with MG vs. HC, and RA vs. HC were made using univariate and multivariate statistics. RESULTS:Serum biomarker patterns were statistically significantly different between groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models exhibited considerable distinction between all groups. Metabolites were then filtered to remove peak pairs common to both disease cohorts. Combined metabolite panels revealed clear separation between MG and HC for both library-matched (AUROC: 0.92 ± 0.03) and highest AUC patients (AUROC: 0.94 ± 0.05). CONCLUSION: In patients presenting to the clinic with seropositive MG, metabolomic profiling is capable of distinguishing patients with disease from those without. These results provide an important first step towards a potential biomarker for improving MG identification.
RCT Entities:
INTRODUCTION:Myasthenia gravis (MG) is a chronic, potentially debilitating autoimmune disease characterized by weakness and rapid fatigue of the voluntary muscles that worsens on exertion. Left untreated, MG symptoms may cause significant morbidity or even death. To date, no robust biological marker is available to follow the course of the disease. Therefore, new diagnostic approaches and biological markers are essential not only for improved diagnosis of the disease but for improved outcomes. OBJECTIVES: The present study applied a two-control, multi-label metabolomics profiling approach as a potential strategy for the identification of biomarkers unique to myasthenia gravis (MG). METHODS: Metabolic analyses using acid- and dansyl-labelled serum from seropositive MG (n = 46), rheumatoid arthritis (RA) (n = 23) and healthy controls (HC) (n = 49) were performed on samples from adult patients presenting to the University of Alberta Hospital neuromuscular and rheumatology clinics. Comparisons between patients with MG vs. HC, and RA vs. HC were made using univariate and multivariate statistics. RESULTS: Serum biomarker patterns were statistically significantly different between groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models exhibited considerable distinction between all groups. Metabolites were then filtered to remove peak pairs common to both disease cohorts. Combined metabolite panels revealed clear separation between MG and HC for both library-matched (AUROC: 0.92 ± 0.03) and highest AUC patients (AUROC: 0.94 ± 0.05). CONCLUSION: In patients presenting to the clinic with seropositive MG, metabolomic profiling is capable of distinguishing patients with disease from those without. These results provide an important first step towards a potential biomarker for improving MG identification.