S-Space College of Medicine/School of Medicine (의과대학/대학원) Obstetrics & Gynecology (산부인과전공) Journal Papers (저널논문_산부인과학전공)
Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery
- Romero, Roberto; Mazaki-Tovi, Shali; Vaisbuch, Edi; Kusanovic, Juan Pedro; Gomez, Ricardo; Yoon, Bo Hyun; Luo, Jingqin; Ryals, John; Beecher, Chris; Banks, David; Mazor, Moshe; Nien, Jyh Kae; Chaiworapongsa, Tinnakorn
- Issue Date
- INFORMA HEALTHCARE
- JOURNAL OF MATERNAL-FETAL & NEONATAL MEDICINE; Vol.23 12; 1344-1359
- Preterm labor; preterm delivery; intraamniotic inflammation; amniocentesis; MIAC; chorioamnionitis; intraamniotic infection; omics sciences; high-dimensional biology; cytokines; microbial invasion of the amniotic cavity; pregnancy
- Objective. Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the `metabolic network` of cells (metabolome) is now possible by using a `metabolomics` approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI). Study Design. Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second. Results. (1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide). Conclusion. Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.
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