Liquid biopsies based on cfDNA have shown success in detecting cancer, but their potential in other diseases has remained largely unexplored. In the new study, investigators used whole-genome sequencing to analyze cfDNA fragmentomes from 1,576 people with liver disease and other comorbidities, examining DNA from across their entire genomes. They examined fragment size and how the fragments were distributed across the genome, including in previously uncharacterized repetitive regions, to look for signs of disease.

In each analysis, roughly 40 million fragments spanning thousands of genomic regions were evaluated—more data than almost any other liquid biopsy test. Machine-learning algorithms were used to sort through these large-scale data to identify disease-specific fragmentation signatures. This AI technology allowed the team to zero in on the most informative patterns and develop a classifying system that detected early liver disease, advanced fibrosis and cirrhosis with high sensitivity.