The placenta produces numerous miRNAs with some of them being released in the maternal circulation. These miRNA genes are encoded into specific clusters and expressed preferentially by placental cells, in a time-dependent manner. They were shown to be dysregulated in plasma and placenta from women suffering from GDM and associated with pregnancy and birth-related outcomes. The discovery of pregnancy-related miRNAs and their respective characterization will provide us with important information as to their function in maternal and placental metabolic regulation. More studies are needed to determine whether they will be useful for early screening of GDM.

IntroductionGestational diabetes mellitus (GDM) is a consequence of an imbalance between insulin sensitivity (IS) and secretion during pregnancy. MicroRNAs (miRNAs) are small and secreted RNA molecules stable in blood and known to regulate physiological processes including glucose homeostasis. The aim of this study was to identify plasmatic miRNAs detectable in early pregnancy predicting IS at 24th-29th week of pregnancy.Research design and methodsWe quantified circulating miRNAs in 421 women in plasma collected at 9.6±2.2 weeks of pregnancy using next-generation sequencing.Resultswe detected 2170 miRNAs: 39 (35 positively and 4 negatively) were associated with IS as estimated by the Matsuda Index at 26.4±1.0 weeks of pregnancy. Lasso regression identified 18 miRNAs independently predicting Matsuda Index-estimated IS. Together with gestational age, maternal age and body mass index at first trimester, they explain 36% of IS variance in late second trimester of pregnancy. These miRNAs regulate fatty acid biosynthesis and metabolism among other pathways.ConclusionsIn summary, we have identified first trimester plasmatic miRNAs predictive of Matsuda Index-estimated IS in late second trimester of pregnancy. These miRNAs could also contribute to initiate and support IS adaptation to pregnancy potentially through lipid metabolism regulation.

AimsOur objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.MethodsWe quantified miRNA using next-generation sequencing in discovery (Gen3G: n = 443/GDM = 56) and replication (3D: n = 139/GDM = 76) cohorts. We have diagnosed GDM using a 75-g oral glucose tolerance test and the IADPSG criteria. We applied stepwise logistic regression analysis among replicated miRNAs to build prediction models.ResultsWe identified 17 miRNAs associated with GDM development in both cohorts. The prediction performance of hsa-miR-517a-3p|hsa-miR-517b-3p, hsa-miR-218-5p, and hsa-let7a-3p was slightly better than GDM classic risk factors (age, BMI, familial history of type 2 diabetes, history of GDM or macrosomia, and HbA1c) (AUC 0.78 vs. 0.75). MiRNAs and GDM classic risk factors together further improved the prediction values [AUC 0.84 (95% CI 0.73–0.94)]. These results were replicated in 3D, although weaker predictive values were obtained. We suggest very low and higher risk GDM thresholds, which could be used to identify women who could do without a diagnostic test for GDM and women most likely to benefit from an early GDM prevention program.ConclusionsIn summary, three miRNAs combined with classic GDM risk factors provide excellent prediction values, potentially strong enough to improve early detection and prevention of GDM.