Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus

Abstract

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. • Gestational diabetes mellitus (GDM) exhibits distinct cfDNA physical properties • Dynamic cfDNA variations between GDM and controls • Altered exocrine pancreas identified by islet acinar marker gene PRSS1 • CfDNA links between GDM and altered lipid metabolism Tang et al. present a longitudinal integrative analysis of cell-free DNA, revealing a distinctive signature and dynamic patterns specific to gestational diabetes mellitus. Lipidomic alterations and changes in pancreatic exocrine markers are discerned.