Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study.

BACKGROUND: Clinicians lack objective tests to help determine the severity of bronchiolitis or to distinguish a viral from bacterial causes of respiratory distress. We hypothesized that children with respiratory syncytial virus (RSV) infection would have a different metabolomic profile compared to those with bacterial infection or healthy controls, and this might also vary with bronchiolitis severity. METHODS: Clinical information and urine-based metabolomic data were collected from healthy age-matched children (n = 37) and those admitted to hospital with a proven infection (RSV n = 55; Non-RSV viral n = 16; bacterial n = 24). Nuclear magnetic resonance (NMR) measured 86 metabolites per urine sample. Partial least squares discriminant analysis (PLS-DA) was performed to create models of separation. RESULTS: Using a combination of metabolites, a strong PLS-DA model (R2 = 0.86, Q2 = 0.76) was created differentiating healthy children from those with RSV infection. This model had over 90 % accuracy in classifying blinded infants with similar illness severity. Two other models differentiated length of hospitalization and viral versus bacterial infection. CONCLUSION: While the sample sizes remain small, this is the first report suggesting that metabolomic analysis of urine samples has the potential to become a diagnostic aid. Future studies with larger sample sizes are required to validate the utility of metabolomics in pediatric patients with respiratory distress.

Metabolomic profiling of asthma and chronic obstructive pulmonary disease: A pilot study differentiating diseases.

BACKGROUND: Differentiating asthma from other causes of chronic airflow limitation, such as chronic obstructive pulmonary disease (COPD), can be difficult in a typical outpatient setting. The inflammation of asthma typically is different than that of COPD, and the degree of inflammation and cellular damage varies with asthma severity. Metabolomics is the study of molecules created by cellular metabolic pathways. OBJECTIVES: We hypothesized that the metabolic activity of adults with asthma would differ from that of adults with COPD. Furthermore, we hypothesized that nuclear magnetic resonance spectroscopy (NMR) would measure such differences in urine samples. METHODS: Clinical and urine-based NMR data were collected on adults meeting the criteria of asthma and COPD before and after an exacerbation (n = 133 and 38, respectively) and from patients with stable asthma or COPD (n = 54 and 23, respectively). Partial least-squares discriminant analysis was performed on the NMR data to create models of separation (86 metabolites were measured per urine sample). Some subjects' metabolomic data were withheld from modeling to be run blindly to determine diagnostic accuracy. RESULTS: Partial least-squares discriminant analysis of the urine NMR data found unique differences in select metabolites between patients with asthma and those with COPD seen in the emergency department and even in follow-up after exacerbation. By using these select metabolomic profiles, the model could correctly diagnose blinded asthma and COPD with greater than 90% accuracy. CONCLUSION: This is the first report showing that metabolomic analysis of human urine samples could become a useful clinical tool to differentiate asthma from COPD.

Identifying hypoxia in a newborn piglet model using urinary NMR metabolomic profiling.

Establishing the severity of hypoxic insult during the delivery of a neonate is key step in the determining the type of therapy administered. While successful therapy is present, current methods for assessing hypoxic injuries in the neonate are limited. Urine Nuclear Magnetic Resonance (NMR) metabolomics allows for the rapid non-invasive assessment of a multitude breakdown products of physiological processes. In a newborn piglet model of hypoxia, we used NMR spectroscopy to determine the levels of metabolites in urine samples, which were correlated with physiological measurements. Using PLS-DA analysis, we identified 13 urinary metabolites that differentiated hypoxic versus nonhypoxic animals (1-methylnicotinamide, 2-oxoglutarate, alanine, asparagine, betaine, citrate, creatine, fumarate, hippurate, lactate, N-acetylglycine, N-carbamoyl-ß-alanine, and valine). Using this metabolomic profile, we then were able to blindly identify hypoxic animals correctly 84% of the time compared to nonhypoxic controls. This was better than using physiologic measures alone. Metabolomic profiling of urine has potential for identifying neonates that have undergone episodes of hypoxia.

Metabolomic profiling of asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy.

BACKGROUND: The ability to diagnose and monitor asthma on the basis of noninvasive measurements of airway cellular dysfunction is difficult in the typical clinical setting. OBJECTIVE: Metabolomics is the study of molecules created by cellular metabolic pathways. We hypothesized that the metabolic activity of children with asthma would differ from healthy children without asthma. Furthermore, children having an asthma exacerbation would be different compared with children with stable asthma in outpatient clinics. Finally, we hypothesized that (1)H-nuclear magnetic resonance (NMR) would measure such differences using urine samples, one of the least invasive forms of biofluid sampling. METHODS: Children (135 total, ages 4-16 years) were enrolled, having met the criteria of healthy controls (C), stable asthma in the outpatient clinic (AO), or unstable asthma in the emergency department (AED). Partial least squares discriminant analysis was performed on the NMR data to create models of separation (70 metabolites were measured/urine sample). Some NMR data were withheld from modeling to be run blindly to determine possible diagnostic accuracy. RESULTS: On the basis of the model of AO versus C, 31 of 33 AO samples were correctly diagnosed with asthma (94% accuracy). Only 1 of 20 C samples was incorrectly labeled as asthma (5% misclassification). On the basis of the AO versus AED model, 31 of the 33 AO samples were correctly diagnosed as outpatient asthma (94% accurate). CONCLUSION: This is the first report suggesting that (1)H-NMR analysis of human urine samples has the potential to be a useful clinical tool for physicians treating asthma.