Prediction Tool Estimates Risk of Gestational Diabetes in Obese Women
SCIENTISTS have developed a prediction tool for early identification of obese women at a high risk of gestational diabetes (GDM) which can be used to guide treatment interventions to those most likely to benefit.
The team developed three different models by drawing on a range of clinical and demographic measures, candidate biomarkers, and a targeted nuclear magnetic resonance (NMR) metabolome. They tested their models by obtaining clinical data and non-fasting blood samples from 1,303 obese pregnant women at 15(+0)–18(+6) weeks’ gestation, enrolled in the UPBEAT trial. Of these, 337 (25.9%) developed GDM and the diagnosis in the majority (72%) of women was based on elevated fasting glucose.
The first model was based on clinical variables associated with GDM, including age, ethnicity, and having previously had GDM. The researchers felt this model showed good discrimination of identifying patients who went on to develop GDM as high-risk (area under curve [AUC]: 0.71, 95% confidence interval [CI]: 0.68–0.74). This improved in the second model to 0.77 (AUC, 95% CI: 0.73–0.80) with the addition of candidate biomarkers: HbA1c, glucose, fructosamine, triglycerides, adiponectin and SHBG. The third model did not improve upon the performance of the second model (AUC: 0.77, 95% CI: 0.74–0.81) which included 158 metabolites.
When the researchers defined the high-risk subgroup by setting the estimated risk for GDM at ≥35%, they found that approximately 50% of this group (Model 2, n=805; Model 3, n=770) progressed to GDM. In these two models, they also found it correctly identified around 80% of those not developing GDM as not at risk.
In their paper, the team highlighted that all obese women are categorised as being at equally high risk of GDM although the majority do not go on to develop the disorder. The predictive model that they have developed could instead offer an easy-to-use and accessible tool offer early identification of women at risk and enable early intervention. “The clinical risk factors in the simple tools are quick to measure with minimal training, and the biomarkers, HbA1c and adiponectin are readily accessible for routine clinical laboratory measurement,” they explained.
Jack Redden, Reporter