Early diagnosis of gestational diabetes mellitus using circulating microRNAs

in European Journal of Endocrinology
Correspondence should be addressed to N Shomron; Email: nshomron@post.tau.ac.il
Restricted access

Design

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and its prevalence is constantly rising worldwide. Diagnosis is commonly in the late second or early third trimester of pregnancy, though the development of GDM starts early; hence, first-trimester diagnosis is feasible.

Objective

Our objective was to identify microRNAs that best distinguish GDM samples from those of healthy pregnant women and to evaluate the predictive value of microRNAs for GDM detection in the first trimester.

Methods

We investigated the abundance of circulating microRNAs in the plasma of pregnant women in their first trimester. Two populations were included in the study to enable population-specific as well as cross-population inspection of expression profiles. Each microRNA was tested for differential expression in GDM vs control samples, and their efficiency for GDM detection was evaluated using machine-learning models.

Results

Two upregulated microRNAs (miR-223 and miR-23a) were identified in GDM vs the control set, and validated on a new cohort of women. Using both microRNAs in a logistic-regression model, we achieved an AUC value of 0.91. We further demonstrated the overall predictive value of microRNAs using several types of multivariable machine-learning models that included the entire set of expressed microRNAs. All models achieved high accuracy when applied on the dataset (mean AUC = 0.77). The significance of the classification results was established via permutation tests.

Conclusions

Our findings suggest that circulating microRNAs are potential biomarkers for GDM in the first trimester. This warrants further examination and lays the foundation for producing a novel early non-invasive diagnostic tool for GDM.

 

     European Society of Endocrinology

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Figures

  • View in gallery

    Differentially expressed miRNAs in GDM vs. control samples. Each of the expressed miRNAs in GDM vs control samples was tested for differential expression in the entire dataset, as well as in both subsets by the county in which the samples were collected: Spain and Italy. Two miRNAs were found to be differentially expressed (adjusted P value < 0.05). Normalized counts of the differentially expressed miRNA are presented as box plots. The upper and lower limits of the boxes represent the 75th and 25th percentiles. The upper and lower whiskers represent maximum and minimum values. The median is indicated by the line in each box. Outliers are indicated by circles.

  • View in gallery

    Validations for the upregulated miRNAs. RT-qPCR was performed on a new cohort of 20 samples: ten GDM samples and ten control samples. RT-qPCR relative expression values (2−ΔCt) of miR-223 and miR-23a are presented as box plots. Counts were normalized to obtain an average expression of 1 in the control group. The upper and lower limits of the boxes represent the 75th and 25th percentiles. The upper and lower whiskers represent maximum and minimum values. The median is indicated by the line in each box. Outliers are indicated by circles.

  • View in gallery

    Classification results for Spain-Italy’s original and permutated datasets using the random forest model. Density plots of statistical measures obtained by 100 iterations of the classification procedure on real (blue) and permutated (red) data sets. Permutated datasets included the same samples after random shuffling of their conditions (i.e., GDM/control). Permutations test means are indicated as well. Sensitivity: true positives out of all positives; Specificity: true negatives out of all negatives; Accuracy: true classifications out of all classifications; Matthews’s correlation coefficient (MCC): a correlation coefficient between the observed and predicted binary classifications; AUC: area under the ROC curve; F1 Score: the harmonic mean of precision and sensitivity; Positive Likelihood Ratio: sensitivity / (1 − specificity); Negative Likelihood Ratio: (1 − sensitivity) / specificity.

  • View in gallery

    The expression of the differentially expressed miRNAs in the placenta and adipose tissues. Normalized counts of miR-223, and mir-23a in 3 tissue types obtained from GDM and control women: (A) placenta, (B) visceral adipose tissue, and (C) subcutaneous adipose tissue. Counts were obtained from NanoString nCounter miRNA profiling.

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