Identification of Pre‐Diagnostic Protein Biomarkers for Liver Cirrhosis Based on Prospective Analysis of a Large‐Scale Plasma Proteomics in the UK Biobank

PURPOSE: Liver fibrosis and cirrhosis represent critical stages in the progression of chronic liver disease, yet their key molecular features remain incompletely understood. EXPERIMENTAL DESIGN: We performed large-scale Olink-based proteomic profiling in over 40,000 participants from the UK Biobank with a median follow-up of 15.6 years to elucidate disease pathophysiology and identify pre-diagnostic biomarkers. Cross-sectional analysis included 66 prevalent cirrhosis cases, and prospective analysis identified 224 incident cirrhosis cases. Machine learning and Mendelian randomization (MR) were applied. An independent cohort was used for validation. RESULTS: Distinct dysregulated proteins were observed in compensated cirrhosis (CC) and decompensated cirrhosis (DC). In the prospective analysis, 696 proteins were associated with disease onset. A proteomic panel based on these markers achieved an AUC of 0.832 for predicting incident cirrhosis, outperforming established fibrosis scores including FIB-4, APRI, and NFS, and demonstrated robust performance across CC and DC populations. The protein panel showed predictive value (AUC = 0.743) for disease progression in an independent cohort. MR identified 66 proteins with putative causal roles, including 11 potential therapeutic targets. CONCLUSIONS AND CLINICAL RELEVANCE: These findings provide novel molecular insights into cirrhosis development and support integrated proteomic biomarkers as a discovery and prioritization framework for early risk stratification.