Early Identification of At-Risk Patients: ProteomicSignature Predicts Progression to DecompensatedCirrhosis

Background & AimsEarly identification of decompensation in patients with cirrhosis is important to enable timely detection, management of complications and for effective treatment. This study investigates the biology of decompensation and aim to identify protein biomarkers for identification of high-risk patients. MethodsThe primary analysis included plasma samples from 46 patients with metabolic dysfunction associated steatotic liver disease (MASLD) related cirrhosis. Plasma samples were depleted for the top 14 most abundant proteins and the proteome was measured by liquid chromatography tandem mass spectrometry. The dataset was divided into a training (14 compensated, 10 decompensated) and a test cohort of compensated patients (11 progressing to decompensation, 11 remaining compensated). Changes in protein levels were determined by ANCOVA and a prognostic model was developed using logistic regression. External validation was performed in an independent cohort of 120 patients with alcohol-related cirrhosis. Time-to-event analyses were conducted in this cohort using Cox regression. Results52 proteins involved in impaired hepatic function, fibrogenesis, immune activation, and metabolic changes were significantly different between compensated and decompensated patients. A prognostic model with four proteins (NBL1, LTBP4, APOC4, GHR), demonstrated predictive ability for future decompensation (AUC=0.93, 73% sensitivity, 100% specificity). In the external validation cohort, the model demonstrated generalizability (AUC=0.78, 72% sensitivity, 82% specificity). Validation cohort time-to-event analyses showed that higher baseline scores were associated with shorter time to liver-related events (HR 1.32; log-rank p = 0.027), underscoring the panels prognostic value. ConclusionOur study indicates that patients with decompensated cirrhosis are characterized by proteomic signatures of fibrogenesis and metabolic dysfunction. Capturing these signatures could help identify patients at risk of complications and potentially those eligible for aetiology directed treatment. Impact and ImplicationsAddressing a critical unmet need for early detection of cirrhosis decompensation, our proteomic study identifies a four-protein panel with predictive ability for decompensation. These findings hold significant implications for hepatologists, clinical researchers, and healthcare systems, offering a novel tool to enhance prognostication and refine treatment strategies, potentially facilitating targeted patient monitoring. However, considering the small discovery sample size and the distinct aetiology of the external validation cohort, further validation is essential before broad clinical integration. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/709475v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@6620e2org.highwire.dtl.DTLVardef@f8dfe4org.highwire.dtl.DTLVardef@1331101org.highwire.dtl.DTLVardef@1a195ca_HPS_FORMAT_FIGEXP M_FIG C_FIG