Aims: The burden of chronic liver disease (CLD) is increasing. This study aims to identify protein markers for CLD and its progression, and develop a protein-based risk prediction model.
Materials and methods: We used proteome-wide Mendelian randomization (MR), Bayesian colocalization and summary-data-based MR with proteomic data from deCODE Genetics to identify CLD-related proteins. Multivariable MR was used to assess independent protein effects. Protein-protein interaction, druggability and mediation analyses were conducted to prioritise therapeutic targets. We further constructed a protein risk score to predict CLD and composite hepatic event outcomes, comparing its performance with existing clinical predictors.
Results: Genetically predicted levels of 16, 5 and 4 plasma proteins were associated with metabolic dysfunction-associated steatotic liver disease (MASLD), alcoholic liver disease (ALD) and cirrhosis, respectively. IGSF3, FTCD, DCXR, ADH1B and ACY1 were associated with CLD progression. Genetically predicted five modifiable factors (body mass index, waist-hip ratio, glycated haemoglobin, type 2 diabetes, leisure television watching) were associated with CLD-related proteins. Proteomic-based models showed high predictive performance for ALD (C-index = 0.89), liver cancer (C-index = 0.84) and liver failure (C-index = 0.84) in a healthy population without liver diseases at baseline.
Conclusions: This study identified key circulating protein markers for CLD. Protein-based profiling demonstrated strong predictive potential for CLD and related outcomes.
Keywords: drug target; liver disease; prediction model; proteome‐wide Mendelian randomization.
© 2026 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.