Dudzik Danuta, Atanasova Vangeliya, Barbas Coral, Bartha Jose Luis
Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland.
Division of Maternal and Fetal Medicine, Fundación Para la Investigación Biomédica, La Paz University Hospital, Madrid, Spain.
Front Mol Biosci. 2025 Jan 15;11:1452312. doi: 10.3389/fmolb.2024.1452312. eCollection 2024.
Gestational diabetes mellitus (GDM) is a global health concern with significant short and long-term complications for both mother and baby. Early prediction of GDM, particularly late-onset, is crucial for implementing timely interventions to mitigate adverse outcomes. In this study, we conducted a comprehensive metabolomic analysis to explore potential biomarkers for early GDM prediction.
Plasma samples were collected during the first trimester from 60 women: 20 with early-onset GDM, 20 with late-onset GDM, and 20 with normal glucose tolerance. Using advanced analytical techniques, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS), we profiled over 150 lipid species and central carbon metabolism intermediates.
Significant metabolic alterations were observed in both early- and late-onset GDM groups compared to healthy controls, with a specific focus on glycerolipids, fatty acids, and glucose metabolism. Key findings revealed a 4.0-fold increase in TG(44:0), TG(46:0), TG(46:1) with -values <0.001 and TG(46:2) with 4.7-fold increase and -value <0.0001 as well as changes in several phospholipids as PC(38:3), PC(40:4) with 1.4-fold increase, < 0.001 and PE(34:1), PE(34:2) and PE(36:2) with 1.5-fold change, < 0.001 in late-onset GDM.
Observed lipid changes highlight disruptions in energy metabolism and inflammatory pathways. It is suggested that lipid profiles with distinct fatty acid chain lengths and degrees of unsaturation can serve as early biomarkers of GDM risk. These findings underline the importance of integrating metabolomic insights with clinical data to develop predictive models for GDM. Such models could enable early risk stratification, allowing for timely dietary, lifestyle, or medical interventions aimed at optimizing glucose regulation and preventing complications such as preeclampsia, macrosomia, and neonatal metabolic disorders. By focusing on metabolic disruptions evident in the first trimester, this approach addresses a critical window for improving maternal and fetal outcomes. Our study demonstrates the value of metabolomics in understanding the metabolic perturbations associated with GDM. Future research is needed to validate these biomarkers in larger cohorts and assess their integration into clinical workflows for personalized pregnancy care.
妊娠糖尿病（GDM）是一个全球性的健康问题，对母婴均有显著的短期和长期并发症。早期预测GDM，尤其是晚发型GDM，对于实施及时干预以减轻不良后果至关重要。在本研究中，我们进行了全面的代谢组学分析，以探索早期预测GDM的潜在生物标志物。
在孕早期收集了60名女性的血浆样本：20名早发型GDM患者、20名晚发型GDM患者和20名糖耐量正常者。使用先进的分析技术，包括液相色谱-串联质谱（LC-MS/MS）和气相色谱-质谱（GC-MS），我们对150多种脂质种类和中心碳代谢中间体进行了分析。
与健康对照组相比，早发型和晚发型GDM组均观察到显著的代谢改变，特别关注甘油脂质、脂肪酸和葡萄糖代谢。主要发现显示，TG(44:0)、TG(46:0)、TG(46:1)增加了4.0倍，P值<0.001，TG(46:2)增加了4.7倍，P值<0.0001，以及几种磷脂发生变化，如晚发型GDM中PC(38:3)、PC(40:4)增加了1.4倍，P<0.001，PE(34:1)、PE(34:2)和PE(36:2)变化了1.5倍，P<0.001。
观察到的脂质变化突出了能量代谢和炎症途径的紊乱。建议具有不同脂肪酸链长度和不饱和度的脂质谱可作为GDM风险的早期生物标志物。这些发现强调了将代谢组学见解与临床数据相结合以开发GDM预测模型的重要性。这样的模型可以实现早期风险分层，从而能够及时进行饮食、生活方式或医学干预，旨在优化血糖调节并预防子痫前期、巨大儿和新生儿代谢紊乱等并发症。通过关注孕早期明显的代谢紊乱，这种方法解决了改善母婴结局的关键窗口期。我们的研究证明了代谢组学在理解与GDM相关的代谢扰动方面的价值。未来需要在更大的队列中验证这些生物标志物，并评估它们在个性化孕期护理临床工作流程中的整合情况。
