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

Abstract

Aims: Our objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.

Methods: We 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.

Results: We 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.

Conclusions: In summary, three miRNAs combined with classic GDM risk factors provide excellent prediction values, potentially strong enough to improve early detection and prevention of GDM.

Keywords: biomarkers; epigenetics; next-generation sequencing; pregnancy; ribo-hormones; risk factors.

Figure 1

First trimester plasmatic miRNAs associated with GDM. Fold change represents the change in plasmatic miRNA abundance in GDM compared to normoglycemic women. Model adjusted for sequencing runs and lanes as well as gestational age. FDR cutoff of 0.1 is represented by a horizontal dotted line. FDR, false discovery rate; NS, non-significant.

Figure 2

ROC curves for prediction of the risk of developing GDM. (A) Models including miRNAs as well as classical risk factors and biomarkers of GDM in the Gen3G cohort test set. History of GDM also includes history of macrosomia. (B) Models with classical risk factors and biomarkers of GDM in the Gen3G cohort test set. History of GDM also includes history of macrosomia (C) Models including miRNAs as well as classical risk factors of GDM in the replication cohort (3D). They are compared with the DeLong test. BMI, body mass index; GCT, 1-h post–50-g glucose challenge test value; GDM, Gestational diabetes mellitus; HbA1c, Glycated hemoglobin; T2D, Type 2 diabetes.

Figure 3

Threshold for identification of women at higher and lower risk of developing GDM. Green and red bars represent the women at a very low and higher risk of developing GDM.

Figure 4

KEGG pathways targeted by miRNAs associated with GDM in both Gen3G and 3D cohorts. KEGG pathways are ranked by their FDR adjusted q-value. Pathways enriched with miRNAs negatively associated with GDM are shown as red bars and the number inside each bar represents the number of miRNAs regulating the pathway. ECM, extracellular matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 5

Venn diagram of first trimester miRNAs associated with GDM and insulin sensitivity as well as being expressed from the C19MC. Blue circle represents miRNAs associated with insulin sensitivity assessed with the Matsuda index between the 24th and 29th week of pregnancy. Red circle represents miRNAs associated with GDM. Green circle represents C19MC miRNAs. C19MC, Chromosome 19 miRNA cluster; GDM, Gestational diabetes mellitus.