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Additional file 3: Table S3. List of circRNAs and miRNAs included in the study.

Additional file 1: Table S1. Primer sequences.

Additional file 2: Table S2. List of all differentially expressed mRNAs (DEMs).

Additional file 4: Figure S1. Enrichment analysis. Cluster diagrams of enriched biological processes (A), cellular components (B), and molecular functions (C) of the GO terms and KEGG pathways (D). The horizontal axis represents the pathway name, while the vertical axis represents the enriched miRNAs. The gradient color represents the log (P-value).

Additional file 6: Figure S3. circ_0078619 and circ_0087961 expression in the gestational diabetes mellitus (GDM, n = 9) and normal glucose tolerance (NGT, n = 9) groups as determined by qPCR. The cell experiment was repeated three times. *** P

Additional file 3: Table S3. Clinical characteristics of patients included in the study.

Additional file 1: Table S1. Primer sequences.

Additional file 5: Figure S2. The intersection of of KEGG pathways according to circRNAs function in a miRNA-dependent manner and in a miRNA-independent manner.

Runyu Du, Na Wu, Ling Li Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People’s Republic of ChinaCorrespondence: Ling LiDepartment of Endocrinology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, People’s Republic of ChinaTel +86 18940251181Fax +86 24-25944460Email liling8864@hotmail.comAbstract: Gestational diabetes mellitus (GDM), one of the most common complications during pregnancy, is associated with a high risk of short- and long-term adverse effects on the mother and offspring. Placenta-derived hormones and cytokines aggravate maternal insulin resistance (IR) during pregnancy, which in turn contribute to GDM. The hyperglycemia and IR in GDM result in aberrant placental structure and function adversely affecting fetal growth and well-being. Therefore, it is reasonable to assume that structural and functional alterations in the placenta contribute to the pathogenesis of GDM and GDM-related complications. Increasing evidence suggests that multiple non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs, and circular RNAs, are dysregulated in placentas of patients with GDM and linked to abnormal placental structure, metabolism, and function. Manipulation of ncRNA expression led to some key pathophysiological features of GDM, such as trophoblast dysfunction, changes in intracellular glucose metabolism, and inflammation. Moreover, placenta-specific ncRNAs may be potential diagnostic biomarkers and even therapeutic targets for GDM. This review summarizes data published on the involvement of aberrantly expressed placental ncRNAs in GDM and provides information on their role in the pathogenesis of GDM and GDM-associated complications.Keywords: dysregulated ncRNA, pregnancy disorder, placental dysfunction, pregnancy-related complications, trophoblast dysfunction

Runyu Du, Ling Li Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People’s Republic of ChinaCorrespondence: Ling LiDepartment of Endocrinology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, People’s Republic of ChinaTel +86 18940251181Fax +86 24-25944460Email liling8864@hotmail.comPurpose: This study sought to develop a nomogram for the prediction of insulin requirement in a Chinese population with gestational diabetes mellitus (GDM).Materials and Methods: We performed a retrospective cohort study involving 626 Chinese women with GDM, of whom 188 were treated with insulin. “Least absolute shrinkage and selection operator” regression was used to optimize the independent predictors of insulin requirement during pregnancies complicated with GDM. Cox proportional hazards regression analysis was performed to establish a prediction model incorporating the selected predictors, and the nomogram was constructed to achieve individual prediction. The C-index, calibration plot and decision curve analysis were used to validate the model.Results: Maternal age, family history of type 2 diabetes mellitus in a first-degree relative, a prior GDM history, fasting plasma glucose, hemoglobin A1c, gestational age, and body mass index values at the time of GDM diagnosis were the risk factors for insulin treatment. The model displayed medium predictive power with a C-index of 0.77 (95% confidence interval: 0.73– 0.81) and relatively good calibration accuracies. The decision curve demonstrated a positive net benefit with a threshold between 0.09 and 0.70.Conclusion: The findings suggest that our nomogram, incorporating seven indicators, is useful in predicting individualized survival probabilities of insulin requirement.Keywords: pregnancy, glycemic control, insulin therapy, prediction