Clinical Epidemiology (Mar 2022)
Identification of Biomarkers for Preeclampsia Based on Metabolomics
Abstract
Mengxin Yao,1 Yue Xiao,1 Zhuoqiao Yang,1 Wenxin Ge,1 Fei Liang,1 Haoyue Teng,1 Yingjie Gu,2 Jieyun Yin1 1Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China; 2Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of ChinaCorrespondence: Jieyun Yin, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou, Jiangsu, People’s Republic of China, Tel/Fax +86 0512 6588036, Email [email protected]: Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE.Methods: This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.Results: Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE.Conclusion: This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.Keywords: gestational hypertension, metabolite, biomarker, prediction
