Huajie Zhang1, Yuxi Zhao1, Danqing Zhao2, Xinqian Chen1, Naseer Ullah Khan1, Xukun Liu1, Qihong Zheng1, Yi Liang2, Yuhua Zhu2, Javed Iqbal1, Jing Lin1,3, Liming Shen4,5. 1. College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China. 2. Department of Obstetrics and Gynecology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People's Republic of China. 3. Shenzhen Key Laboratory of Marine Biotechnology and Ecology, Shenzhen, 518071, People's Republic of China. 4. College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China. slm@szu.edu.cn. 5. Brain Disease and Big Data Research Institute, Shenzhen University, Shenzhen, 518071, People's Republic of China. slm@szu.edu.cn.
Abstract
INTRODUCTION: Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.
INTRODUCTION: Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.
Authors: Daniel A Enquobahrie; Marie Denis; Mahlet G Tadesse; Bizu Gelaye; Habtom W Ressom; Michelle A Williams Journal: J Clin Endocrinol Metab Date: 2015-09-25 Impact factor: 5.958
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Authors: Irene Cetin; Maria S Nobile de Santis; Emanuela Taricco; Tatjana Radaelli; Cecilia Teng; Stefania Ronzoni; Elena Spada; Silvano Milani; Giorgio Pardi Journal: Am J Obstet Gynecol Date: 2005-02 Impact factor: 8.661