LAUSR

Asthma is a complex syndrome that is influenced by both genetic and environmental factors. Determination of its pathobiology requires investigations on the outcome of transcriptional and translational processes, such as a comprehensive analysis of metabolites. Metabolites are a function of the one’s genetic make-up and environmental influences which provide approximate information of a biosystem’s functional state [1]. The recent advent of several analytical techniques has enabled us to simultaneously examine the global collection of small molecules, including sugars, amino acids, organic acids, and lipids (generally <1,800 daltons), in an organism, tissue, or cell – i.e. the metabolome [2]. Metabolomics is the systematic analysis of the group of functional metabolites that are present in a biological system [2]. There are three major analytical techniques used in metabolomics: nuclear magnetic resonance (NMR) spectroscopy, gas chromatography mass spectrometry (GC-MS), and liquid chromatography mass spectrometry (LC-MS) [3]. Metabolomics is generally applied through either a targeted or global (or untargeted) measurement approach [3]. Targeted metabolomics is a quantitative approach that allows the measurement of specific metabolite concentrations. In contrast, global metabolomics undertakes a simultaneous assessment of metabolites without a priori sample knowledge for hypothesis generation [3]. Global metabolomics is a comprehensive strategy for identifying changes in different pathophysiological states. However, a stringent significance threshold (e.g. Bonferroni or false discovery rate) needs to be applied to account for multiple testing and the discovered metabolites need to be validated in an independent cohort to avoid potential false-positive findings. Additionally, metabolomics studies generate highly collinear and sparse data which may not be handled well by most conventional statistical models. This constitutes statistical and methodological challenges, particularly when combining with another omics data. Thus, choosing appropriate statistical methods for analyzing metabolomics data – such as partial least squares-discriminant analysis (PLS-DA), sparse PLS-DA, and dimension reduction approaches (e.g. principal component analysis) – is essential [4]. Please see Table 1 for a glossary of common terms used in metabolomics. 2. Evidence on metabolomics and asthma etiology