Albumin-bilirubin score as a practical tool for prognostication in metabolic-associated steatotic liver disease

Abstract

Metabolic-associated steatotic liver disease (MASLD) has become the leading cause of chronic liver disease worldwide, yet reliable tools for prognostication remain limited. Fibrosis-based indices such as the fibrosis-4 and nonalcoholic fatty liver disease fibrosis score are widely used but primarily reflect structural damage rather than functional decline. The albumin-bilirubin (ALBI) score, originally established to assess hepatic reserve in patients with hepatocellular carcinoma, provides a simple and objective measure of liver function derived from routine laboratory parameters. Recent validation and meta-analytic studies have shown that ALBI predicts liver-related outcomes and all-cause mortality across diverse chronic liver disease populations, including MASLD, and offers complementary prognostic information beyond fibrosis-based models. Its simplicity, cost-effectiveness, and compatibility with automated reporting systems make it feasible for integration into clinical workflows and population-level risk stratification. However, interpretation of ALBI should consider potential confounders such as renal dysfunction, inflammation, and Gilbert syndrome, and threshold calibration across ethnic groups remains necessary. The ALBI score represents a promising functional biomarker that could enhance risk prediction and care pathways in MASLD, although prospective, multiethnic, and longitudinal studies remain needed to confirm its prognostic value and define clinically meaningful cut-offs.

Keywords: Metabolic-associated steatotic liver disease; Albumin-bilirubin score; Prognosis; Risk stratification; Noninvasive biomarkers

Core Tip: The albumin-bilirubin score is a simple, objective index derived from routine laboratory tests that reflects hepatic functional reserve. In metabolic-associated steatotic liver disease, albumin-bilirubin complements fibrosis-based models by providing functional information that may improve early risk detection and guide referral or monitoring strategies. Its accessibility and cost-effectiveness make it an attractive candidate for integration into clinical workflows and public health frameworks, although further validation and threshold calibration are still needed.