DNA methylation patterns and predictive models for metabolic disease risk in offspring of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common pregnancy complication with far-reaching implications for maternal and offspring health, strongly tied to epigenetic modifications, particularly DNA methylation. However, the precise molecular mechanisms by which GDM increases long-term metabolic disease risk in offspring remain insufficiently understood.

We integrated multiple publicly available whole-genome methylation datasets focusing on neonates born to mothers with GDM. Using differentially methylated positions (DMPs) identified in these datasets, we developed a machine learning model to predict GDM-associated epigenetic changes, then validated its performance in a clinical target cohort.

In the public datasets, we identified DMPs corresponding to genes involved in glucose homeostasis and insulin sensitivity, with marked enrichment in insulin signaling, AMPK activation, and adipocytokine signaling pathways. The predictive model exhibited strong performance in public data (AUC = 0.89) and moderate performance in the clinical cohort (AUC = 0.82). Although CpG sites in the PPARG and INS genes displayed similar methylation trends in both datasets, the small validation cohort did not yield statistically significant differences.

By integrating robust public data with a targeted validation cohort, this study provides a comprehensive epigenetic profile of GDM-exposed offspring. Owing to the limited sample size and lack of statistical significance, definitive conclusions cannot yet be drawn; however, the observed directional consistency suggests promising avenues for future research. Larger and more diverse cohorts are warranted to confirm these preliminary findings, clarify their clinical implications, and enhance early risk assessment for metabolic disorders in children born to GDM mothers.