Abstract
McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n = 16) and control (n = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.
Highlights
Multiple sclerosis (MS) is one of the autoimmune disorders causing demyelination of axons [1, 2]
Quantification of 43 metabolites, 13 amino acids, and 30 acylcarnitines was performed by a targeted quantitative approach with isotope-labeled internal standards
The multiple-reaction monitoring (MRM) mode of data acquisition was chosen for convenient peak integration in the MultiQuant software
Summary
Multiple sclerosis (MS) is one of the autoimmune disorders causing demyelination of axons [1, 2]. Modern diagnosis of MS is based on the revised McDonald criteria including magnetic resonance imaging (MRI) to confirm the result [3]. The development of new methods for the diagnosis and prognosis of MS is a highly relevant research topic. Multivariate statistical analysis is frequently applied to a whole preprocessed metabolomics dataset in metabolomics studies of human diseases, MS [8, 9]. Predictive models involving several statistically significant markers outperform a single-marker model in terms of area under the curve (AUC) metrics and distinguish multiple groups with partially shared markers among them [10]. The advantage of metabolomic profiling was used to separate clinical groups into subgroups, to distinguish the relapsing-remitting type and secondary progressive type of MS [11]
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