Machine Learning-Derived Predictive Risk Score for Prediabetes and Type 2 Diabetes Development in Parous Women

Amélie Taschereau,1 Jenna Wong,2 Soren Harnois-Leblanc,2 Marie A Brunet,3,4 Myriam Doyon,4 Mélina Arguin,4 Sheryl L Rifas-Shiman,2 Emily Oken,2 Patrice Perron,4,5 Pierre-Étienne Jacques,1,4,6 Luigi Bouchard,4,7,8,* Marie-France Hivert2,4,9,* 1Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada; 2Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA; 3Department of Pediatrics, Université de Sherbrooke, Sherbrooke, QC, Canada; 4Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), Sherbrooke, QC, J1H 5N3, Canada; 5Department of Medicine, University of Sherbrooke, Sherbrooke, QC, Canada; 6Institut de recherche sur le cancer de l’Université de Sherbrooke (IRCUS), Sherbrooke, QC, Canada; 7Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada; 8Department of Medical Biology, CIUSSS of Saguenay-Lac-Saint-Jean, Saguenay, QC, Canada; 9Department of Medicine, Massachusetts General Hospital, Boston, MA, USA*These authors contributed equally to this workCorrespondence: Marie-France Hivert, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA, Tel +1 617-580-1487, Email mhivert@mgb.orgBackground/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