DL-beta-Aminoisobutyric Acid

Childhood asthma prevalence continues to rise despite advancements in prevention and medical management strategies. The purpose of this study was to investigate correlations between urinary organic acids and pulmonary diagnostic tests, asthma control in Greek asthmatic children. We hypothesized that urinary organic acids are positively associated with poor pulmonary function in children with asthma. Seventy-two children, 5 to 12 years old with asthma were recruited from a pediatric asthma clinic in Athens, Greece. Pulmonary function was assessed using spirometry and exhaled nitric oxide analysis. Asthma control was measured qualitatively using the Asthma Control Questionnaire. Targeted metabolomic analysis of 34 urinary organic acids in children was conducted by gas chromatography-mass spectrometry. A statistically significant difference between girls and boys was found for asthma control score (P = .02), lactic acid (P = .03), but not for any other organic acids (P > .05). Statistically significant correlations were found between lactic acid and Forced Expiratory Volume in 1 second (FEV1) (P = .02), Forced Vital Capacity (FVC) (P = .03); 4- hydroxyphenylacetic acid and FEV1 (P = .01), FVC (P = .01); 5-hydroxyindoleacetic acid and FEV1/FVC (P = .03), eNO (P = .05); glycolic acid with Peak Expiratory Flow (PEF) (P = .03); and malic acid with asthma control (P = .02). In conclusion, metabolomics was used to determine correlations between urinary organic acids and conventional pulmonary diagnostic tests in Greek asthmatic children. Metabolomics could be a promising approach for asthma research and in detection of novel biomarkers for asthma monitoring and therapeutic targets for childhood asthma. This study contributes towards a better understanding of the biochemical pathways involved in asthma.

Background: The diagnosis of asthma in children is challenging and relies on a combination of clinical factors and biomarkers including methacholine challenge, lung function, bronchodilator responsiveness, and presence of airway inflammation. No single test is diagnostic. We sought to identify a pattern of inflammatory biomarkers that was unique to asthma using a targeted metabolomics approach combined with data science methods. Methods: We conducted a nested case-control study of 100 children living in a peri-urban community in Lima, Peru. We defined cases as children with current asthma, and controls as children with no prior history of asthma and normal lung function. We further categorized enrollment following a factorial design to enroll equal numbers of children as either overweight or not. We obtained a fasting venous blood sample to characterize a comprehensive panel of targeted markers using a metabolomics approach based on high performance liquid chromatography-mass spectrometry. Results: A statistical comparison of targeted metabolites between children with asthma (n = 50) and healthy controls (n = 49) revealed distinct patterns in relative concentrations of several metabolites: children with asthma had approximately 40-50% lower relative concentrations of ascorbic acid, 2-isopropylmalic acid, shikimate-3-phosphate, and 6-phospho-d-gluconate when compared to children without asthma, and 70% lower relative concentrations of reduced glutathione (all p < 0.001 after Bonferroni correction). Moreover, a combination of 2-isopropylmalic acid and betaine strongly discriminated between children with asthma (2-isopropylmalic acid ≤ 13 077 normalized counts/second) and controls (2-isopropylmalic acid > 13 077 normalized counts/second and betaine ≤ 16 47 121 normalized counts/second). Conclusions: By using a metabolomics approach applied to serum, we were able to discriminate between children with and without asthma by revealing different metabolic patterns. These results suggest that serum metabolomics may represent a diagnostic tool for asthma and may be helpful for distinguishing asthma phenotypes.

The diagnosis of asthma and chronic obstructive pulmonary disease (COPD) can be challenging due to the overlap in their clinical presentations in some patients. There is a need for a more objective clinical test that can be routinely used in primary care settings. Through an untargeted 1H NMR urine metabolomic approach, we identified a set of endogenous metabolites as potential biomarkers for the differentiation of asthma and COPD. A subset of these potential biomarkers contains 7 highly polar metabolites of diverse physicochemical properties. To the best of our knowledge, there is no liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that evaluated more than two of the target metabolites in a single analytical run. The target metabolites belong to the families of monosaccharides, organic acids, amino acids, quaternary ammonium compounds and nucleic acids, rendering hydrophilic interaction liquid chromatography (HILIC) an ideal technology for their quantification. Since a clinical decision is to be made from patients data, a fully validated analytical method is required for biomarker validation. Method validation for endogenous metabolites is a daunting task since current guidelines were designed for exogenous compounds. As such, innovative approaches were adopted to meet the validation requirements. Herein, we describe a sensitive HILIC-MS/MS method for the quantification of the 7 endogenous urinary metabolites. Detection was achieved in the multiple reaction monitoring (MRM) mode with polarity switching, using quadrupole-linear ion trap instrument (QTRAP 6500) as well as single ion monitoring in the negative-ion mode. The method was fully validated according to the regulatory guidelines. Linearity was established between 6 and 21000 ng/mL and quality control samples demonstrated acceptable intra- and inter-day accuracy (85.7%-112%), intra- and inter-day precision (CV% <11.5%) as well as stability under various storage and sample processing conditions. To illustrate the method's applicability, the validated method was applied to the analysis of a small set of urine samples collected from asthma and COPD patients. Preliminary modelling of separation was generated using partial least square discriminant analysis (R2 0.752 and Q2 0.57). The adequate separation between patient samples confirms the diagnostic potential of these target metabolites as a proof-of-concept for the differentiation between asthma and COPD. However, more patient urine samples are needed in order to increase the statistical power of the analytical model.