128. Detection of predictive makers for preeclampsia using NMR and GC–MS/MS metabolomics

Introduction The Birth and Three Generation Cohort Study was initiated by Tohoku Medical Megabank Organization (ToMMo), Tohoku University, Japan. A total of 73 085 participants, spanning 3 generations and including 22 493 pregnant women, have been recruited as of January 18, 2018. This cohort study was designed to establish methods for personalized early prediction of multifactorial diseases using cutting-edge technology and an integrated biobank, with a focus on genetic and environmental interactions. Objectives Our aim is to discover early predictors of preeclampsia using nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-tandem mass spectrometry (GC–MS/MS) metabolomics to evaluate maternal plasma samples from the first trimester of pregnancy. Methods Plasma samples collected during the first trimester from pregnant women who later developed preeclampsia were obtained from the ToMMo biobank. There were 298 patients who developed preeclampsia, including 45 with early onset and 253 with late onset; these patients were compared with 338 patients with normal pregnancies, randomly selected from the database. We performed NMR and GC–MS/MS to detect circulating metabolites which could be candidates for early predictors of preeclampsia. The present study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of ToMMo. Results Using NMR metabolomics, 36 metabolites were identified and quantified from each plasma sample. A total of 5 early-onset and 8 late-onset metabolites had significantly different values between the case- and control groups. On GC–MS/MS analysis, 155 metabolites were quantified satisfactorily; 15 early-onset and 31 late-onset metabolites had statistically significant differences between groups. Conclusions We identified several candidate predictors using NMR and GC–MS/MS metabolomics in pregnant women who later developed preeclampsia. These results might bring new insights into the establishment of early-prediction formulae, in combination with additional genetic and epidemiologic information.