Tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) are proinflammatory cytokines and known to be involved in many pathological processes. However, the association between serum levels of TNF-α, IL-6, and pregnancy-induced hypertension (PIH) is unclear. The aim of the present study was to determine the serum levels of TNF-α and IL-6 and to investigate their potential correlation with PIH. In this study, the serum concentrations of TNF-α and IL-6 in pregnant women who developed PIH and normal pregnant women were measured. We found that the serum concentrations of TNF-α and IL-6 were significantly increased in the patients with PIH compared to the normal pregnant women. In addition, elevated TNF-α and IL-6 concentrations were associated with pathological complications. Moreover, in a hypoxia-induced PIH mice model, animals from the PIH group demonstrated higher TNF-α and IL-6 levels when compared to control, and serum TNF-α and IL-6 levels were positively correlated with right ventricular systolic blood pressure. Furthermore, TNF-α and IL-6 levels were decreased when the PIH mice were treated with remodulin compared to control group. In conclusion, our results suggested that high serum TNF-α and IL-6 levels are associated with PIH, and TNF-α and IL-6 might be potential predictors in the prognosis of PIH.

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Pregnancy-induced hypertension (PIH) is also called gestational hypertension, which is developed in the late pregnancy. It is estimated that 7%-10% of pregnancies are affected by PIH which compromises the normal pregnancy process. 1 PIH has been considered as the major cause of perinatal and maternal mortality. 2 The pathogenic mechanisms of PIH remain unclear. One of the major hallmarks in PIH is endothelial dysfunction. Endothelial cells line the internal lumen of the vasculature and participate in

Background. Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods. DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results. We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion. We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.