Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

Abstract

Objective Biomarkers for preterm labor 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 preterm labor (PTL) at risk for preterm delivery, regardless of the presence or absence of intra-amniotic infection/inflammation (IAI). Study Design Two retrospective cross-sectional studies were conducted, including 3 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 by 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 as well as 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.

Random Forest as a Non-parametric Algorithm for Near-infrared (NIR) Spectroscopic Discrimination for Geographical Origin of Agricultural Samples

“…[22][23][24][25][26] Diverse analytical data such as mass spectra and chromatograms has been directly fed into RF for either discrimination or classification. RF combines many trees to form a forest for analysis.…”

Preterm Labor

“…22,27,28 Among patients in PTL, biomarkers for increased risk of preterm delivery have been identified with proteomic profiling, transcriptomics, and metabolomics, both in amniotic fluid and maternal serum. 29,30 Characterization of the identified compounds has shown that different pathologic pathways are involved in PTL besides acute inflammation. 29 Benefits Amniocentesis can establish whether an intrauterine infection is present and, thus, assist the clinician in the counseling of patients, expediting delivery, initiation of guided antibiotic therapy, and alerting the neonatologist to the increased risk of an infected or compromised neonate.…”

Targeted metabolomics and mass spectrometry

“…There are a number of limitations to such analysis, with infusion mass spectrometry potentially suffering from ion suppression effects and empirical formula being tentatively matched to more than one possible biological compound. Despite these limitations, such global, unbiased analysis has been applied to many varied fields of investigation including the study of biomarkers for disorders such as cancer and Parkinson's disease (Caudle et al, 2010;Oakman et al, 2010), preterm labor (Romero et al, 2010), and environmental pollution (Santos et al, 2010). The aim of this chapter is to discuss the application of mass spectrometry to the study of metabolites in a targeted manner.…”