A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes

Abstract

Gestational diabetes mellitus (GDM) affects 3-14% of pregnancies, with 20-50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6-9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non-case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions.

Figure 1

Study design and metabolic assay work flow. A: Study design of the SWIFT prospective cohort, a total of 1,035 women in whom GDM was diagnosed were enrolled at 6–9 weeks postpartum (baseline) and screened via 2-h 75-g OGTTs. At baseline (V1), 21 women with T2D and 4 ineligible women were excluded from the follow-up. The study observed 1,010 participants without diabetes who were rescreened annually via OGTTs with retention rates of 85% and 83% for 1 and 2 years, respectively. Prospective cohort sample sizes for non-T2D and incident T2D are as follows: T2D developed in 59 women at 1 year and in 54 women at 2 years; T2D developed in another 17 women beyond 2–4 years postbaseline. B: Work flow of metabolomics assay. A total of 182 metabolites were assayed in plasma from V1 (baseline) using LC-MS/MS, gas chromatography–mass spectrometry (GC-MS), and ELISA. For further methodology, please refer to Supplementary Table 1.

Figure 2

Decision tree and ROC for the prediction of incident T2D. A: Decision tree by the J48 machine learner based on the combined AUCs and F scores of all algorithms. The gray boxes indicate the metabolite chosen for the node, whereas the clear numbered boxes indicate the concentration threshold in μmol/L for PC ae C40:5, BCAA, and SM (OH) C14:1 and in mmol/L for hexoses. The percentage below each group indicates percent of instances correctly classified. B: ROC curve of the J48 machine learner algorithm for the training and testing sets, performing with discriminative power of 0.830 (P < 0.000001) and 0.769 (P < 0.0001), respectively, which is greater than for FPG alone 0.724 (P < 0.0001) and 0.706 (P < 0.01), as well as for 2hPG alone 0.726 (P < 0.000001) and 0.661 (P < 0.05), respectively. Data are presented as the AUC.

Figure 3

Comparison of Venn diagrams and contingency tables of model predictions of future diabetes. A: Venn diagrams of correct and incorrect predictions of the testing data set for all patients; only patients with incident T2D and patients with non-T2D (Non) are shown. Intersection of correct predictions (green) and incorrect (red) indicates that one or more models had identical prediction of a patient, and the other models did not. Although the correct and incorrect patient predictions appear similar across all three models (left), the glucose and combined models have worse performance for the prediction of future diabetes (middle). The combined model has worse prediction for control subjects (right). B: Contingency tables of the three different models against the testing data set. Columns are known group labels, and rows are predicted group labels. The metabolite model (left) shows the higher P and Sp compared with the glucose model. The combined model (right) has overall poorer Se and Sp compared with both the metabolite and glucose models alone.