The identification of pregnant women with Gestational Diabetes Mellitus (GDM) who will require insulin therapy, may modify their management to closer monitoring and possible early interventions. Findings from a new study published in ‘Nutrition and diabetes’ discloses predictive model for the necessity of insulin treatment in women with GDM.

This prospective cohort study included data from 775 women diagnosed with GDM as per the IADPSG criteria. The data were analyzed using logistic regression and a machine learning algorithm, the Classification and Regression Trees (CART). Potential predictors routinely recorded at follow-up visits were tested and used for the development of the model. The resultant model was externally validated using the data from two different perinatology clinics.

Preconceptional maternal BMI and morning fasting blood glucose levels at baseline and at 1 h during an Oral Glucose Tolerance Test (OGTT) were independent significant predictors for the treatment modality of GDM. Baseline blood glucose greater than 98 mg/dl and preconceptional maternal Body Mass Index (BMI) between 26 and 31 kg/height2 increased substantially the probability of insulin therapy.

The study concluded that a simple model based on maternal characteristics and the values of an OGTT can predict the need for insulin treatment with accuracy.