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

Abstract

BackgroundClinicians 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.MethodsClinical 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.ResultsUsing 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.ConclusionWhile 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.

Using urine metabolomics to understand the pathogenesis of infant respiratory syncytial virus (RSV) infection and its role in childhood wheezing

“…The identified metabolite patterns seen during infant viral infection are consistent with and indicative of active viral infection (Delgado et al 2012; Milner et al 2014; Munger et al 2006; Ritter et al 2010; Sanchez and Lagunoff 2015; Yu et al 2011), but were not specific to RSV ARI. The only other study comparing children with RSV, non-RSV virus, and bacterial infection also found similar metabolic profile differences (Adamko et al 2016). For example, similar to our study, Adamko et al found that betaine was elevated in children with RSV infection compared to both non-infected children and children with non-RSV infection.…”

Metabolomic Profiling of Infants With Recurrent Wheezing After Bronchiolitis

“…Although viruses are known to be capable of reprogramming metabolic pathways, very few published studies have applied metabolomics to bronchiolitis. It was recently demonstrated that analysis of the metabolomic profiles of urine and nasopharyngeal aspirate can differentiate healthy children from those with viral respiratory tract infections [10] and can discriminate between different degrees of disease severity [10,30,31]. The metabolome of nasopharyngeal aspirates obtained from infants with bronchiolitis also revealed its potential for distinguishing between infants infected with HRV as opposed to RSV [32].…”

“…Recently, an approach called "metabolomics" [6,7] has revealed the potential to identify the metabolic features of certain conditions, differentiating between phenotypes of the same disease and enabling a better characterization of the pathological mechanisms involved. Metabolomic studies are typically performed on biofluids, and urine samples are among the most often used in medical research, especially studies of children, because they can be collected using simple, noninvasive techniques [8][9][10].…”

Recent Advances in NMR-Based Metabolomics

“…A first metabolomics application to evaluate urinary metabolic profiles to distinguish bacterial and viral causes in children with respiratory tract infections was presented recently. 130 While the sample size was small, the interesting result was that the urinary metabolome could differentiate these two causes based on statistical modeling. Another study focused on distinguishing Plasmodium falciparum infection from other symptomatically similar diseases by evaluating plasma metabolite profiles.…”