Journal
BMC INFECTIOUS DISEASES
Volume 16, Issue -, Pages -Publisher
BIOMED CENTRAL LTD
DOI: 10.1186/s12879-016-1709-6
Keywords
Metabolomics; Biomarkers; Bronchiolitis; Children; Respiratory syncytial virus; Bacteria
Categories
Funding
- AllerGen
- NCE
- Women's and Children's Health Research Institute
- University of Alberta
- Stollery Children's Hospital Foundation
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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 (R-2 = 0.86, Q(2) = 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.
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