Datasheet1_Metabolic profiles in gestational diabetes mellitus can reveal novel biomarkers for prediction of adverse neonatal outcomes.zip

BackgroundGestational diabetes mellitus (GDM) significantly affects the fetal metabolic environment, elevating risks of neonatal hypoglycemia and macrosomia. Metabolomics offers promising avenues for early prediction and diagnosis of GDM and associated adverse offspring outcomes.
MethodsThis study analyzed serum samples from pregnant women diagnosed with GDM at 24 to 28 weeks of gestation using untargeted metabolomics. We monitored the health outcomes of their offspring to explore the correlation between initial serum metabolite profiles and subsequent health outcomes, to uncover the predictive markers for hypoglycemia and macrosomia in these offspring.
ResultsOut of 200 participants, 154 had normal newborns, 33 had offspring with hypoglycemia, and 19 had offspring with macrosomia. From 448 identified metabolites, 66 showed significant differences in cases of hypoglycemia, and 45 in macrosomia. A panel of serum metabolite biomarkers achieved Area Under the Curve (AUC) values of 0.8712 for predicting hypoglycemia and 0.9434 for macrosomia.
ConclusionThe study delineated metabolic disruptions in GDM during 24–28 weeks of gestation and pinpointed biomarkers capable of forecasting adverse neonatal outcomes. These findings could inform GDM management strategies and minimize the incidence of such outcomes.

背景：妊娠期糖尿病（Gestational diabetes mellitus, GDM）可显著影响胎儿代谢环境，升高新生儿低血糖与巨大儿的发病风险。代谢组学为GDM及其相关子代不良结局的早期预测与诊断提供了极具潜力的研究路径。
方法：本研究采用非靶向代谢组学（untargeted metabolomics）技术，对孕24~28周确诊为GDM的孕妇血清样本进行分析，并随访其子代的健康结局，旨在探究母体初始血清代谢物谱与子代后续健康结局之间的关联，以期挖掘子代低血糖与巨大儿的预测标志物。
结果：本研究共纳入200名受试者，其中154名诞下健康新生儿，33名子代发生低血糖，19名子代罹患巨大儿。在448种鉴定出的代谢物中，低血糖组有66种代谢物存在显著差异，巨大儿组则有45种。一组血清代谢物标志物用于预测低血糖的受试者工作特征曲线下面积（Area Under the Curve, AUC）达0.8712，预测巨大儿的AUC为0.9434。
结论：本研究阐明了孕24~28周GDM患者的代谢紊乱特征，并精准定位了可预测新生儿不良结局的生物标志物。上述研究结果可为GDM临床管理策略提供参考，进而降低此类不良结局的发生率。