Background Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. Methods The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. Results Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72–0.74, accuracy between 0.73–0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. Conclusions The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

During pregnancy, omega 3 supplementation has raised its popularity due to evidence that it would show not only benefits in the neural and visual development of the unborn child, but also in the prevention of obstetrical pathologies associated with of perinatal morbidity and mortality. Omega 3 polyunsaturated fatty acids (PUFAs), specifically, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), possess anti-inflammatory, vasodilatory and anti-aggregating properties, which have led to the use of PUFAs in the prevention of cardiovascular diseases. In this review, we detail the effects of omega 3 supplementation on different aspects of pregnancy such as prevention of preterm birth, pre-eclampsia, postpartum depression, and improved metabolism during gestational diabetes. Although there are several randomized clinical trials using omega-3 supplementation during pregnancy, the evidence remains inconclusive, due to variability in dosage and administration time. Certainly, a greater number of high-quality studies including randomized clinical trials are necessary to determine the impact of omega 3 supplementation during pregnancy in the prevention of obstetric pathologies.