Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

• Main Authors: Renato Teixeira Souza, Jussara Mayrink, Débora Farias Leite, Maria Laura Costa, Iracema Mattos Calderon, Edilberto Alves Rocha Filho, Janete Vettorazzi, Francisco Edson Feitosa, José Guilherme Cecatti
• Format: Article
• Language: English
• Published: Faculdade de Medicina / USP 2019-03-01
• Series: Clinics
• Subjects: Maternal Health (MeSH); Metabolomics (MeSH); Translational Medical Research (MeSH); Prediction
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322019000100303&lng=en&tlng=en

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and “omics” sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent “boom” in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.