Cell-free DNA (cfDNA) fragmentomics, which analyzes cfDNA fragmentation features including size distribution, nucleosome footprints, end motifs, and methylation patterns, enables the inference of tissue origins and pathophysiological status. This approach holds significant value for the early prediction of pregnancy complications such as gestational diabetes mellitus, preterm birth, and preeclampsia. When integrated with non-invasive prenatal testing and machine learning algorithms, this technology provides a novel non-invasive diagnostic tool for clinical application. This review systematically summarizes research advances in cfDNA fragmentomics for the early prediction of pregnancy complications, covering technical principles, clinical applications, and future directions, thereby providing a theoretical foundation for optimizing and expanding its clinical utility.

Cell-free DNA (cfDNA) fragmentomics, which analyzes cfDNA fragmentation features including size distribution, nucleosome footprints, end motifs, and methylation patterns, enables the inference of tissue origins and pathophysiological status. This approach holds significant value for the early prediction of pregnancy complications such as gestational diabetes mellitus, preterm birth, and preeclampsia. When integrated with non-invasive prenatal testing and machine learning algorithms, this technology provides a novel non-invasive diagnostic tool for clinical application. This review systematically summarizes research advances in cfDNA fragmentomics for the early prediction of pregnancy complications, covering technical principles, clinical applications, and future directions, thereby providing a theoretical foundation for optimizing and expanding its clinical utility.