Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia.
Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia.
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DOI：
10.1371/journal.pone.0164211
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Gervasi MT
中科院分区：
文献类型：
--
作者：
Baraldi E;Giordano G;Stocchero M;Moschino L;Zaramella P;Tran MR;Carraro S;Romero R;Gervasi MT
支气管肺发育不良（BPD）是与早产相关的严重并发症。越来越多的证据表明产前因素在其发病机制中发挥着作用。代谢组学可以同时表征低分子量化合物，并可以提供这种复杂状况的图片。本研究的目的是评估羊水 (AF) 的无偏代谢组学分析是否可用于调查后代自发性早产 (PTD) 和 BPD 发展的风险。我们对 32 名婴儿进行了一项探索性研究，这些婴儿的母亲因胎膜完整的自发性早产而在孕 21 至 28 周期间接受了羊膜穿刺术。使用质谱法结合超高效液相色谱法对 AF 样品进行非靶向代谢组学分析。使用多变量和单变量统计数据分析工具对获得的数据进行分析。潜在结构正交约束投影判别分析 (oCPLS2-DA) 排除了对关键临床变量数据建模的影响。 oCPLS2-DA 能够发现足月 (n = 11) 和早产 (n = 13) 分娩之间选定代谢物的独特差异（负电离数据集：R2 = 0.47，预测中的平均 AUC ROC = 0.65；正电离数据集：R2 = 0.47，预测中的平均 AUC ROC = 0.70）以及 PTD 和随后发生 BPD 之间（n = 10），和无 BPD 的 PTD (n = 11)（负数据集：R2 = 0.48，预测中的平均 AUC ROC = 0.73；正数据集：R2 = 0.55，预测中的平均 AUC ROC = 0.71）。这项研究表明，羊水代谢分析可能有助于识别自发性早产和有患 BPD 风险的胎儿。这些发现支持这样的假设：一些产前代谢失调可能在 PTD 的发病机制和 BPD 的发展中发挥关键作用。
Bronchopulmonary dysplasia (BPD) is a serious complication associated with preterm birth. A growing body of evidence suggests a role for prenatal factors in its pathogenesis. Metabolomics allows simultaneous characterization of low molecular weight compounds and may provide a picture of such a complex condition. The aim of this study was to evaluate whether an unbiased metabolomic analysis of amniotic fluid (AF) can be used to investigate the risk of spontaneous preterm delivery (PTD) and BPD development in the offspring. We conducted an exploratory study on 32 infants born from mothers who had undergone an amniocentesis between 21 and 28 gestational weeks because of spontaneous preterm labor with intact membranes. The AF samples underwent untargeted metabolomic analysis using mass spectrometry combined with ultra-performance liquid chromatography. The data obtained were analyzed using multivariate and univariate statistical data analysis tools. Orthogonally Constrained Projection to Latent Structures-Discriminant Analysis (oCPLS2-DA) excluded effects on data modelling of crucial clinical variables. oCPLS2-DA was able to find unique differences in select metabolites between term (n = 11) and preterm (n = 13) deliveries (negative ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.65; positive ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.70), and between PTD followed by the development of BPD (n = 10), and PTD without BPD (n = 11) (negative data set: R2 = 0.48, mean AUC ROC in prediction = 0.73; positive data set: R2 = 0.55, mean AUC ROC in prediction = 0.71). This study suggests that amniotic fluid metabolic profiling may be promising for identifying spontaneous preterm birth and fetuses at risk for developing BPD. These findings support the hypothesis that some prenatal metabolic dysregulations may play a key role in the pathogenesis of PTD and the development of BPD.
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