Background/Aims: Pregnancy is a window of opportunity for closer links with clinical care, and to identify women at risk of chronic disease. Because of the elevated risk of type 2 diabetes (T2D) associated with gestational diabetes mellitus (GDM), most existing prediction models for post-delivery T2D focus on women with GDM, leaving many parous women without clear risk stratification. This study aimed to develop a prediction model for prediabetes or T2D risk in the general population of parous women, based on clinical pregnancy variables. Methods: We assessed prediabetes/T2D five years after delivery in the Genetics of Glucose Regulation in Gestation and Growth (Gen3G) cohort (N=403). Using a machine-learning approach, we developed a risk prediction model from which we derived a simple, clinically usable risk index: the Gestational 4-variable Prediabetes/type 2 diabetes (G4PD) index. The G4PD index was then validated in the Project Viva cohort at three years (n=562) and seventeen years (n=541) after delivery. Results: The G4PD index included gestational weight gain, pre-gestational body mass index, first-trimester maternal age, and a GDM variable reflecting hyperglycemia severity during pregnancy. In Gen3G, the model achieved a cross-validated estimate of the area under the receiver operating characteristic curve (ROC-AUC) of 0.696. The G4PD index achieved ROC-AUC of 0.682 in the 17-year Project Viva dataset, with similar results in the 3-year dataset. Beyond overall discrimination, the model effectively stratified women into clinically meaningful risk categories, with those in the lowest group (< 2) exhibiting an expected risk of ~2% and ~15% at three and seventeen years after delivery, respectively, whereas those with the highest scores (≥ 7 or ≥ 5) expected substantially higher risks (~7% and ~37% at respective time points). Conclusion: The G4PD index, derived from clinical pregnancy variables, moderately predicts the risk of prediabetes/T2D over several years. Keywords: pregnancy, prediabetes, type 2 diabetes, risk stratification, prediction model, machine-learning algorithm
Taschereau et al. (Sun,) studied this question.