Abstract
The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.
Highlights
Metabolites are small molecules that are the substrates, intermediates, or end products of metabolism
Hundreds of altered metabolic features were identified in ataxia–telangiectasia (A–T) patients upon treatment with nicotinamide riboside, which included vitamin B3-associated metabolites
Chemometric methods can distinguish between samples obtained before and during treatment; assigning all the metabolic features to metabolites and connecting them to disease mechanisms is still a challenging task
Summary
Metabolites are small molecules that are the substrates, intermediates, or end products of metabolism. Alterations in metabolite concentration may be related to a disease or may be caused by xenobiotics [1]. Untargeted metabolomics aims to measure as many metabolites as possible and is often used as a hypothesis-generating or an exploratory tool. One popular analytical technique for untargeted metabolomics is LC/MS, which can generate thousands of features. The majority of these features remains unidentified, i.e., their masses do not match any known metabolite present in metabolite databases [2]. Alterations of a single enzyme may lead to a cascade of secondary metabolic effects, which are measured in untargeted metabolomics but their impact on disease presentation and development is unclear [3]
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