First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus

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.

Automated summary

The study aims to identify and predict plasmatic miRNAs associated with and predictive of GDM during the first trimester of pregnancy. Next-generation sequencing was used to quantify miRNA in discovery and replication cohorts. Seventeen miRNAs were identified as being associated with GDM development in both cohorts, and certain miRNAs showed slightly better prediction performance than classic GDM risk factors. Combining miRNAs and classic risk factors further improved the prediction values.