Abstract

As a well-known fact to the public, gestational diabetes mellitus (GDM) could bring serious risks for both pregnant women and infants. During this important investigation into the linkage between GDM patients and their altered expression in the serum, proteomics techniques were deployed to detect the differentially expressed proteins (DEPs) of in the serum of GDM patients to further explore its pathogenesis, and find out possible biomarkers to forecast GDM occurrence. To investigation serum proteins differentially expressed in GDM were assessed using isobaric tag for relative and absolute quantitation (iTRAQ) proteomics and bioinformatics analyses. Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria. Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation, and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry. Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis, and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA). Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDM gravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest 16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteins associated with lipid metabolism, coagulation cascade activation, complement system and inflammatory response in the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serum of GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk of gestation. GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complement system and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

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