College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen 518083, China.
Tianjin Women and Children's Health Center, Tianjin 300070, China.
Cell Rep Med. 2024 Aug 20;5(8):101660. doi: 10.1016/j.xcrm.2024.101660. Epub 2024 Jul 25.
Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.
妊娠期糖尿病（GDM）在整个孕期表现多样，具有复杂的临床挑战。高通量游离 DNA（cfDNA）测序分析有望增进我们对 GDM 发病机制和预测的理解。在 299 名 GDM 患者和 299 名匹配的健康孕妇中，我们在整个孕期中发现了与 GDM 相关的独特 cfDNA 片段特征。将 cfDNA 图谱与脂质组学和单细胞转录组学数据相结合，阐明了与 GDM 中脂质代谢过程改变相关的功能变化。将 50 个特征基因的转录起始位点（TSS）评分用作 cfDNA 特征，可有效区分 GDM 病例和对照组。值得注意的是，胰岛腺泡标记基因 PRSS1 的差异覆盖成为 GDM 的有价值的生物标志物。我们开发了一种专门的神经网络模型，可在两个独立队列中预测 GDM 的发生并进行验证。这项研究强调了高通量 cfDNA 对 GDM 的早期预测和特征描述，为其分子基础及其潜在的临床应用提供了新的见解。
