Prediction of gestational diabetes in obese pregnant women from the UK Pregnancies Better Eating and Activity (UPBEAT) pilot trial

Aim:
To examine the prediction of gestational diabetes in obese women using routine clinical measures and measurement of biomarkers related to insulin resistance in the early second trimester.

Methods: 
A total of 117 obese pregnant women participating in a pilot trial of a complex intervention of dietary advice and physical activity were studied. Blood samples were obtained at recruitment (15(+0)-17(+6) weeks' gestation) and demographic, clinical history and anthropometric measures recorded. The biomarkers analysed were plasma lipids (HDL cholesterol, LDL cholesterol, triglycerides), high-sensitivity C-reactive protein, alanine transaminase, aspartate transaminase, ferritin, fructosamine, insulin, adiponectin, tissue plasminogen activator, interleukin-6, visfatin and leptin. Univariate and logistic regression analyses were performed to determine independent predictors and area under the receiver-operating curve was calculated for the model.

Results: 
Of the 106 participants included in the analysis, 29 (27.4%) developed gestational diabetes. Participants with gestational diabetes were older (P = 0.002), more often of parity >= 2, had higher systolic (P = 0.02) and diastolic blood pressure (P = 0.02) and were more likely to be black (P = 0.009). Amongst the blood biomarkers measured, plasma adiponectin alone remained independently associated with gestational diabetes in adjusted models (P = 0.002). The area under the receiver-operating curve for clinical factors alone (0.760) increased significantly (area under the curve 0.834, chi-square statistic (1) = 4.00, P = 0.046) with the addition of adiponectin.

Conclusions: 
A combination of routinely measured clinical factors and adiponectin measured in the early second trimester in obese women may provide a useful approach to the prediction of gestational diabetes. Validation in a large prospective study is required to determine the usefulness of this algorithm in clinical practice.