Autor(es):
Souza, Renato Teixeira ; Mayrink, Jussara ; Leite, Débora Farias ; Costa, Maria Laura ; Calderon, Iracema Mattos [UNESP] ; Filho, Edilberto Alves Rocha ; Vettorazzi, Janete ; Feitosa, Francisco Edson ; Cecatti, José Guilherme ; Baker, Philip N. ; Kenny, Louise ; Sulek, Karolina ; Parpinelli, Mary A. ; Franchini, Kleber G. ; Melo, Elias ; Anacleto, Danilo ; Cassettari, Bianca F. [UNESP] ; Vernini, Joice [UNESP] ; Pfitscher, Lucia ; Lucena, Daisy
Data: 2019
Identificador Persistente: http://hdl.handle.net/11449/188938
Origem: Oasisbr
Assunto(s): Maternal health (MeSH); Metabolomics (MeSH); Prediction; Translational medical research (MeSH)
Descrição
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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.
Departamento de Ginecologia e Obstetricia Faculdade de Ciencias Medicas Universidade Estadual de Campinas
Departamento Materno Infantil Faculdade de Medicina Universidade Federal de Pernambuco
Departamento de Ginecologia e Obstetricia Faculdade de Medicina de Botucatu Universidade Estadual de Sao Paulo (UNESP)
Departamento de Ginecologia e Obstetricia Faculdade de Medicina Universidade Federal do Rio Grande do Sul
Departamento de Ginecologia e Obstetricia Faculdade de Medicina Universidade Federal do Ceara
University of Leicester
University of Cork
Gravida: National Center for Growth & Development Liggins Institute University of Auckland
School of Medical Sciences University of Campinas
LNBio
Department of Mother and Child Health School of Medicine Federal University of Pernambuco
School of Medicine of Botucatu UNESP
Hospital de Clínicas de Porto Alegre and School of Medicine Federal University of Rio Grande do Sul
School of Medicine Federal University of Ceara
Departamento de Ginecologia e Obstetricia Faculdade de Medicina de Botucatu Universidade Estadual de Sao Paulo (UNESP)
School of Medicine of Botucatu UNESP
