Scientific Reports · 2020 · 21 citations · 44 references
Life SciencesBiochemistryNeurologyClinical BiochemistryMass Spectrometric AnalysisMetabolomicsBiomarker ValidationClinical MetabolomicsMass Spectrometry-based MetabolomicsMetabolite ProfilingMetabolite QuantificationMultiple SclerosisClinical Metabolite ProfilingDiagnostic Accuracy StudiesDiagnostic Testing AccuracyDisease Metabolite ProfilingMetabolite BiomarkersMetabolite-based DiagnosticsMetabolomic PhenotypingSerum Biomarkers
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.
44
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Rating neurologic impairment in multiple sclerosis
John F. Kurtzke · Neurology · 1983 · 14.9K citations
New Multiple Sclerosis Phenotypic Classification
Fred Lublin · European Neurology · 2014 · 4.3K citations
HMDB 4.0: the human metabolome database for 2018
David S. Wishart, Yannick Djoumbou-Feunang, Ana Marcu et al. · Nucleic Acids Research · 2017 · 3.6K citations · Full text
Defining the clinical course of multiple sclerosis
Fred Lublin, Stephen C. Reingold, Jeffrey A. Cohen et al. · Neurology · 2014 · 3.1K citations · Full text