A data-driven framework reconstructs the molecular continuum of human MASLD progression
In brief
Blood test with 57 genes predicts advanced MASLD fibrosis better than existing scores
Researchers built a data-driven molecular trajectory from liver transcriptomes and linked it to plasma proteins, creating a 57-gene blood panel that accurately identifies patients with advanced fibrosis and places them along the disease continuum. In independent cohorts the panel outperformed standard non-invasive scores, offering a more precise tool for staging and therapeutic decision-making, though prospective validation is still needed.
- Journal
- Nature metabolism (Q1)
- Published
- 14 July 2026
- Study design
- Cross-sectional study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Ioannis Kamzolas, Thodoris Koutsandreas, Charlie George Barker, Anna Vathrakokoili Pournara, Harry Weston, Naoto Fujiwara, et al.
- PMID
- 42448794
- DOI
- 10.1038/s42255-026-01543-7
Why clinicians should know about it
- Picked for Histology (top studies of the week, 19 July 2026).
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) progresses along a continuum from simple steatosis to steatohepatitis, fibrosis, cirrhosis and hepatocellular carcinoma. However, current clinical and research frameworks rely primarily on static, histology-defined stages that fail to capture the continuous nature of disease progression. Here, we present a data-driven framework that reconstructs MASLD progression as a continuous molecular trajectory from cross-sectional liver transcriptomic profiles. By positioning patients along this trajectory, we move beyond conventional stage-based classifications and resolve the ordered activation of regulatory programmes, signalling pathways and cellular remodelling processes underlying disease progression. To enable non-invasive patient stratification, we integrate the inferred molecular trajectory with paired liver-plasma proteomics data and identify a 57-gene plasma-accessible biomarker panel that accurately predicts advanced fibrosis and continuously positions patients along the disease trajectory across independent cohorts, outperforming established non-invasive clinical scores. Together, this work establishes a generalizable trajectory-based framework for understanding MASLD pathophysiology and provides a foundation for mechanistically informed biomarker discovery, precision staging and stage-aware therapeutic prioritization.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.