Wang Na, Li Suping, Yang Li
Department of Internal Medicine, Jiaxing Maternity and Child Health Care Hospital, Jiaxing Zhejiang, 314051, China.
Fetal Medicine Center, Jiaxing Maternity and Child Health Care Hospital, Jiaxing, 314051, Zhejiang, China.
Diabetol Metab Syndr. 2025 May 2;17(1):147. doi: 10.1186/s13098-025-01707-7.
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.
妊娠期糖尿病（GDM）是一种常见的妊娠并发症，对母婴健康有着深远影响，与表观遗传修饰密切相关，尤其是DNA甲基化。然而，GDM增加后代长期代谢疾病风险的确切分子机制仍未得到充分理解。
我们整合了多个公开可用的全基因组甲基化数据集，这些数据集聚焦于患有GDM的母亲所生的新生儿。利用这些数据集中确定的差异甲基化位点（DMP），我们开发了一种机器学习模型来预测与GDM相关的表观遗传变化，然后在一个临床目标队列中验证其性能。
在公开数据集中，我们确定了与参与葡萄糖稳态和胰岛素敏感性的基因相对应的DMP，在胰岛素信号传导、AMPK激活和脂肪细胞因子信号通路中显著富集。该预测模型在公开数据中表现出较强性能（AUC = 0.89），在临床队列中表现出中等性能（AUC = 0.82）。尽管PPARG和INS基因中的CpG位点在两个数据集中显示出相似的甲基化趋势，但小型验证队列未产生统计学上的显著差异。
通过将可靠的公开数据与目标验证队列相结合，本研究提供了暴露于GDM的后代的全面表观遗传概况。由于样本量有限且缺乏统计学意义，目前尚不能得出明确结论；然而，观察到的方向一致性为未来研究提供了有前景的途径。需要更大、更多样化的队列来证实这些初步发现，阐明其临床意义，并加强对GDM母亲所生孩子代谢紊乱的早期风险评估。
