Role of serum biomarkers to optimise a validated clinical risk prediction tool for gestational diabetes.

Background: Clinical risk prediction tools for gestational diabetes (GDM) may be enhanced by measuring biomarkers in early pregnancy. Aim: To evaluate a two‐step GDM risk prediction tool incorporating fasting glucose (FG) and serum biomarkers in early pregnancy. Materials and methods: High molecular weight (HMW) adiponectin, omentin‐1 and interleukin‐6 (IL‐6) were measured at 12–15 weeks gestation in women with high risk of GDM from a randomised trial using a clinical risk prediction tool. GDM diagnosis (24–28 weeks) was evaluated using 1998 Australian Diabetes in Pregnancy (ADIPS) criteria and newer International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria. Associations between biomarkers and development of GDM were examined using multivariable regression analysis. Area under the receiver‐operator curve (AUC), sensitivity and specificity were calculated to determine classification ability of each model compared to FG and maternal characteristics. Results: HMW adiponectin improved prediction of ADIPS GDM (AUC 0.85, sensitivity 50%, specificity 96.2%, P = 0.04), compared to FG and maternal factors (0.78, 35% and, 98.1%, respectively). HMW adiponectin <1.53 μg/mL further improved the model (AUC 0.87, sensitivity 75%, specificity 88.2%, P = 0.01). HMW adiponectin did not improve prediction of IADPSG GDM (AUC 0.84, sensitivity 64%, specificity 97.9%, P = 0.22) compared to FG and maternal factors (0.79, 56%, 93.8%). Omentin‐1 and IL‐6 did not significantly improve classification ability for GDM. Conclusions: A two‐step approach combining FG and HMW adiponectin to a validated clinical risk prediction tool improved sensitivity and predictive ability for ADIPS GDM. Further research is required to enhance GDM prediction using IADPSG criteria for application in clinical practice. [ABSTRACT FROM AUTHOR]