Guihong Liu1,2, Weinan Deng1,2, Wei Cui1,2, Qian Xie1,2, Guili Zhao1,2, Xunwei Wu1,2, Lijuan Dai1,2, Dunjin Chen1,2, Bolan Yu1,2. 1. Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. 2. Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Abstract
Objectives: To analyze and compare concentrations of amino acids (AAs) and acylcarnitine (AC) profiles in maternal-fetal serum from women with preeclampsia (PE) and to assess their use as possible predictors of PE. Methods: This is a retrospective study in which we enrolled a total of 38 pregnant women and their offspring. Pregnant women with PE (n = 14) and healthy pregnant control subjects (n = 24) participated voluntarily in the study. Maternal blood and cord blood were tested using dry blood spot (DBS) specimens, and we detected concentrations of 18 types of AAs and 31 types of AC by using high-performance liquid chromatography tandem mass spectrometry (HPLC-MS), and compared metabolites between the groups. We used logistic regression modeling to estimate the association of each metabolite with development of PE. Results: Concentrations of most AAs and AC in PE mothers were significantly higher than those in the group of control mothers. Cord plasma concentrations of AC in most PE mothers were significantly higher than those in controls; however, in PE, levels of cord plasma concentrations of most AAs were significantly lower, except for Gly, compared with controls. Levels of most AAs and AC were lower in the control and PE groups, with a tendency for lower levels in maternal blood compared to cord blood. Receiver operating characteristics (ROC) and areas under the curves (AUC) analyses using these metabolites did not predict PE individually.Conclusions: Maternal-fetal levels of AAs and AC were associated with PE. But the use of metabolites did not constitute a reliable method for use as a biomarker in the diagnosis of PE. Further prospective studies are needed to clarify the roles of different metabolites involved in the mechanism underlying the development of PE.
Objectives: To analyze and compare concentrations of amino acids (AAs) and acylcarnitine (AC) profiles in maternal-fetal serum from women with preeclampsia (PE) and to assess their use as possible predictors of PE. Methods: This is a retrospective study in which we enrolled a total of 38 pregnant women and their offspring. Pregnant women with PE (n = 14) and healthy pregnant control subjects (n = 24) participated voluntarily in the study. Maternal blood and cord blood were tested using dry blood spot (DBS) specimens, and we detected concentrations of 18 types of AAs and 31 types of AC by using high-performance liquid chromatography tandem mass spectrometry (HPLC-MS), and compared metabolites between the groups. We used logistic regression modeling to estimate the association of each metabolite with development of PE. Results: Concentrations of most AAs and AC in PE mothers were significantly higher than those in the group of control mothers. Cord plasma concentrations of AC in most PE mothers were significantly higher than those in controls; however, in PE, levels of cord plasma concentrations of most AAs were significantly lower, except for Gly, compared with controls. Levels of most AAs and AC were lower in the control and PE groups, with a tendency for lower levels in maternal blood compared to cord blood. Receiver operating characteristics (ROC) and areas under the curves (AUC) analyses using these metabolites did not predict PE individually.Conclusions: Maternal-fetal levels of AAs and AC were associated with PE. But the use of metabolites did not constitute a reliable method for use as a biomarker in the diagnosis of PE. Further prospective studies are needed to clarify the roles of different metabolites involved in the mechanism underlying the development of PE.
Authors: Alex Chao; Jarod Grossman; Celeste Carberry; Yunjia Lai; Antony J Williams; Jeffrey M Minucci; S Thomas Purucker; John Szilagyi; Kun Lu; Kim Boggess; Rebecca C Fry; Jon R Sobus; Julia E Rager Journal: Environ Int Date: 2022-06-30 Impact factor: 13.352
Authors: Xiyao Liu; Huijia Fu; Li Wen; Fangyu Zhu; Yue Wu; Zhi Chen; Richard Saffery; Chang Chen; Hongbo Qi; Chao Tong; Philip N Baker; Mark D Kilby Journal: Front Mol Biosci Date: 2022-04-08