Improving cardiovascular risk prediction in metabolic liver disease with a novel biomarker-enhanced model
Year: 2025
Type: article
Study design: Observational Study
Abstract: AIM: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), and its more severe form, Metabolic Dysfunction-Associated Steatohepatitis (MASH), pose significant global health challenges. Conventional cardiovascular risk models, such as the ASCVD Risk Estimator Plus, are limited in accurately predicting CVD risk in MASLD/MASH patients. This study aims to enhance the predictive accuracy of cardiovascular events in MASLD/MASH patients by developing a novel regression model, the LIVER-ASCVD+ model, which integrates traditional cardiovascular risk factors with liver biomarkers. METHODS: A retrospective cohort study was conducted using data from 9,185 biopsy-confirmed MASH patients within an integrated delivery network in the US. The study compared the performance of the LIVER-ASCVD+ model against the ASCVD Risk Estimator Plus. Kaplan-Meier survival analysis was conducted to assess outcomes, with comparisons made to two propensity-matched non-MASH control cohorts. RESULTS: The LIVER-ASCVD+ model demonstrated superior predictive accuracy for myocardial infarction (MI)/stroke events (AUC: 0.68) and mortality (AUC: 0.63) compared to the ASCVD Risk Estimator Plus (MI/stroke AUC: 0.63; mortality AUC: 0.54). The model stratified patients into high and low-risk categories, with significant differences observed in 10-year MI/stroke incidence and mortality rates. Kaplan-Meier analyses further validated the improved performance of the LIVER-ASCVD+ model in predicting cardiovascular outcomes. CONCLUSION: The integration of liver-specific biomarkers into cardiovascular risk assessment models for MASLD/MASH patients significantly enhances predictive accuracy. The LIVER-ASCVD+ model represents a promising approach to improving clinical decision-making and patient outcomes in MASLD/MASH, warranting further validation.
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funder: Madrigal Pharmaceuticals
OpenAlex ID: W7119806560