When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.

Arachidonic acid (AA) and docosahexaenoic acid (DHA) play important roles in inflammation and disease progression, where AA is viewed as proinflammatory and DHA as antiinflammatory. We observe in our model of allergic asthma that the AA/DHA ratio is significantly skewed in a proinflammatory direction. Fenretinide, a vitamin A derivative, has been shown to correct fatty acid imbalances in other diseases. Therefore, we explored if fenretinide can have a protective effect in allergic asthma. To accomplish this, we measured the levels of AA and DHA in the lungs of nonallergic, ovalbumin-induced allergic, and fenretinide-treated allergic mice. We also investigated the effect of allergic asthma and fenretinide treatment on markers of oxidative stress, levels of metabolites, IgE production, airway hyperresponsiveness, and histological changes. Our data demonstrate that treatment of allergen-sensitized mice with fenretinide before allergen challenge prevents ovalbumin-induced changes in the AA/DHA ratio. The levels of several metabolites, such as serotonin, and markers of cellular stress, which are increased after ovalbumin challenge, are also controlled by fenretinide treatment. We observed the protective effect of fenretinide against ovalbumin-induced airway hyperresponsiveness and inflammation in the lungs, illustrated by a complete block in the infiltration of inflammatory cells to the airways and dramatically diminished goblet cell proliferation, even though IgE remained high. Our results demonstrate that fenretinide is an effective agent targeting inflammation, oxidation, and lung pathology observed in allergic asthma.

Background. Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. Methods. A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. Results. All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. Conclusion. This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.