Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study.
Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study.
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DOI：
10.1186/s12879-016-1709-6
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发表时间：
2016-08-22
影响因子：
3.7
通讯作者：
Robinson JL
中科院分区：
文献类型：
--
作者：
Adamko DJ;Saude E;Bear M;Regush S;Robinson JL
临床医生缺乏客观的测试来帮助确定毛细支气管炎的严重程度或区分呼吸窘迫的病毒和细菌原因。我们假设呼吸道合胞病毒（RSV）感染儿童的代谢组学特征与细菌感染儿童或健康对照组的代谢组学特征不同，这也可能因细支气管炎的严重程度而异。临床信息和基于尿液的代谢组学数据来自年龄匹配的健康儿童（n = 37）和证实感染RSV的入院儿童（n = 55；非RSV病毒n = 16；细菌n = 24）。核磁共振（NMR）测量了每个尿液样本中的86种代谢物。采用偏最小二乘判别分析（PLS-DA）建立分离模型。利用代谢产物的组合，建立了一个强大的PLS-DA模型（R2 = 0.86, Q2 = 0.76）来区分健康儿童和RSV感染儿童。该模型在对疾病严重程度相似的失明婴儿进行分类时准确率超过90%。另外两个模型区分了住院时间和病毒感染与细菌感染。虽然样本量仍然很小，但这是第一份表明尿液样本代谢组学分析有可能成为诊断辅助手段的报告。未来需要更大样本量的研究来验证代谢组学在儿科呼吸窘迫患者中的应用。
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. 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. 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. 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.