Predictive plasma biomarker for gestational diabetes: A case-control study in China.

OBJECTIVE: This study aims to find new plasma biomarkers in early pregnancy. DESIGN: The original study enrolled 1219 pregnant women. We investigated protein expression profiles of placental tissues from women with GDM (n = 89) and normal glucose tolerance (NGT) (n = 83). Maternal plasma samples between two groups in early and middle pregnancy were used for validation of candidate biomarkers. METHODS: Differentially expressed proteins (DEPs) were identified by label-free quantitative proteomics from human placenta samples between two groups. Several DEPs were validated in plasma by Luminex assays. An automatic biochemical analyzer was used to detect blood lipid indexes. The associations of GAL-3BP with biochemical indicators were demonstrated by Pearson's correlation analysis. Binary logistic regression was used to model potential predictive indicators in early pregnancy of GDM. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy of the predictive model and the value of GAL-3BP. RESULTS: 123 DEPs were found in placenta involved in ribosomal function, pancreatic secretion, oxidative phosphorylation, and inflammatory signaling pathway. Plasma GAL-3BP are significantly higher in women with GDM than NGT in the first (p = 0.008) and second (p = 0.026) trimester, but C9 and VWF have no difference. The predictive value of GAL-3BP in the first trimester of pregnancy (AUC 0.64) is better than that in the second trimester (AUC 0.61), and combined predictive model of TG and GAL-3BP at early pregnancy has greater predictive and diagnostic value for GDM (AUC 0.69) than individual GAL-3BP (AUC 0.64). CONCLUSIONS: Plasma TG and GAL-3BP has good predictive and diagnostic value at early pregnancy, suggesting that these two indicators may be used as biomarkers for early prediction and diagnosis of GDM. SIGNIFICANCE: The advantage of this study is that circulating TG and GAL-3BP might differentiate the progress of women with GDM and normal glucose tolerance (NGT) at the early stage of pregnancy. It is the first study to consider the role of GAL-3BP as an early predictive biomarker in the development of GDM during the whole pregnancy. Another advantage is that volunteers in this study were recruited from two provinces in China to eliminate the impacts of environmental confounders. The similar changes of blood glucose/lipid indicators for women with GDM and NGT in both regions was found in the first and second trimester of pregnancy, which added to the reliability of analytical results.

Placental DNA methylation changes in gestational diabetes mellitus.

In this study, we investigated the association between altered methylation in the maternal placenta and hyperglycaemia and explored the epigenetic mechanisms underlying gestational diabetes mellitus (GDM). Reduced representation bisulphite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on placental tissues obtained from women with GDM and healthy controls. Further, pyrosequencing, correlation analyses, and linear regression analyses were performed to valuate relationships between aberrantly methylated-differentially expressed genes and clinical parameters. The EMBOSS and JASPAR databases were used for a computational analysis of CpG islands and transcription factor-binding sites in the TRIM67 promoter region. A CpG island with a length of 264 bp in the placental TRIM67 promoter region in the GDM group exhibited significant hypermethylation at four CpG sites. The hypermethylation of the TRIM67 promoter region in the maternal placenta showed a significant, positive correlation with the 1 h and 2 h oral glucose tolerance test (OGTT) values and a negative correlation with lipoprotein(a). Placental DNA methylation levels in the TRIM67 promoter region were markedly elevated in GDM and were associated with blood glucose and lipid levels during healthy pregnancy.

Epigenetic alternations of microRNAs and DNA methylation contribute to gestational diabetes mellitus.

This study aimed to identify epigenetic alternations of microRNAs and DNA methylation for gestational diabetes mellitus (GDM) diagnosis and treatment using in silico approach. Data of mRNA and miRNA expression microarray (GSE103552 and GSE104297) and DNA methylation data set (GSE106099) were obtained from the GEO database. Differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs) and differentially methylated genes (DMGs) were obtained by limma package. Functional and enrichment analyses were performed with the DAVID database. The protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Simultaneously, a connectivity map (CMap) analysis was performed to screen potential therapeutic agents for GDM. In GDM, 184 low miRNA-targeting up-regulated genes and 234 high miRNA-targeting down-regulated genes as well as 364 hypomethylation-high-expressed genes and 541 hypermethylation-low-expressed genes were obtained. They were mainly enriched in terms of axon guidance, purine metabolism, focal adhesion and proteasome, respectively. In addition, 115 genes (67 up-regulated and 48 down-regulated) were regulated by both aberrant alternations of miRNAs and DNA methylation. Ten chemicals were identified as putative therapeutic agents for GDM and four hub genes (IGF1R, ATG7, DICER1 and RANBP2) were found in PPI and may be associated with GDM. Overall, this study identified a series of differentially expressed genes that are associated with epigenetic alternations of miRNA and DNA methylation in GDM. Ten chemicals and four hub genes may be further explored as potential drugs and targets for GDM diagnosis and treatment, respectively.