Large-scale plasma proteomics improves prediction of heart failure among MASLD individuals: A prospective cohort study.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) substantially elevates the risk of heart failure (HF). While large-scale proteomics improves HF prediction in general populations, its incremental predictive value beyond standard clinical models in MASLD remains unexplored. Objective: To identify plasma protein biomarkers for incident HF in MASLD and evaluate the predictive utility of integrating these signatures with the predicting risk of cardiovascular disease events (PREVENT) clinical model. Methods: We prospectively analyzed 17,091 individuals with MASLD at baseline. Multivariable and LASSO-Cox regressions were applied to 2911 plasma proteins to identify optimal predictors. Predictive discrimination and reclassification were assessed using Harrell's C-index, time-dependent area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results: Over a median follow-up of 13.56 years, 953 incident HF events occurred. Integrating the PREVENT model with a 37-protein panel substantially improved predictive discrimination (C-index 0.805 vs. 0.723; ΔC-index 0.082, 95%CI: 0.064-0.100). Moreover, a parsimonious model containing only 5 proteins (NT-proBNP, WFDC2, LTBP2, BCAN, HAVCR1) delivered a meaningful incremental improvement over the PREVENT baseline (C-index 0.769 vs. 0.723; ΔC-index 0.046, 95%CI: 0.027-0.064). Pathway analyses indicated these proteins associating with systemic inflammation and extracellular matrix remodeling. Conclusions: Large-scale proteomics significantly enhances HF risk prediction in MASLD, providing a robust tool for identifying high-risk individuals who may benefit from intensive clinical monitoring and preventive strategies.