Context: Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m2.
Objective: To improve the prediction of GDM using metabolomics.
Methods: We performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m2 in the POP study.
Results: Forty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10-12, 2.2 × 10-13, 6.9 × 10-28, and 2.6 × 10-17, respectively). In the POP study, BMI > 30 kg/m2 had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86).
Conclusions: We identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.
Keywords: gestational diabetes mellitus; metabolomics; prediction; pregnancy.
© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.