Data-Driven Phenotypic Clusters of Gestational Diabetes Mellitus and Associations With Risk of Perinatal Complications and Postpartum Diabetes

OBJECTIVE: Management of gestational diabetes mellitus (GDM) largely follows a uniform approach, despite growing recognition of GDM heterogeneity. We aimed to identify data-driven GDM clusters by using machine learning techniques and clinical data and to assess their associations with perinatal complications and postpartum diabetes risk. RESEARCH DESIGN AND METHODS: In a population-based cohort study, 37,544 individuals with GDM were followed up through 12 years postpartum. In the discovery (70%) and validation (30%) sets, we applied dimension reduction and clustering methods using routinely available sociodemographic, behavioral, and clinical variables. Covariate-adjusted modified Poisson and Cox regression models were used to assess associations of GDM clusters with risk of perinatal complications and postpartum diabetes. RESULTS: Four data-driven GDM phenotypic clusters were identified. Cluster 1 (C1) (65.6%), C2 (14.5%), C3 (12.0%), and C4 (7.8%) comprised the discovery set, with similar distributions in the validation set (C1-C4 66.7%, 14.0%, 12.0%, 7.4%, respectively). C2-C4 compared with C1 (late-diagnosed, lower-BMI, and postload hyperglycemia GDM) were associated with higher risks of perinatal complications and new-onset postpartum diabetes, especially C4 (early-diagnosed, comorbidity-related, and high-glucose challenge test GDM) (adjusted relative risks: severe maternal morbidity 1.43 [95% CI 1.19, 1.72] and neonatal intensive unit admission 1.53 [1.41, 1.66]; adjusted hazard ratio for diabetes 4.32 [95% CI 3.94, 4.73]). Within the largest cluster C1, three subclusters were identified, with differential risks of perinatal complications but not postpartum diabetes. CONCLUSIONS: Our study identified distinct data-driven GDM phenotypic clusters with differential risks of perinatal complications and postpartum diabetes. These findings may inform personalized risk assessment and management strategies tailored to GDM phenotypic clusters to possibly reduce adverse health outcomes.