Clin Mol Hepatol. 2026 Aug 28. doi: 10.3350/cmh.2026.0398. Online ahead of print.
ABSTRACT
Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction and alcohol-related liver disease (MetALD) exist on a continuous spectrum in which fibrosis stage remains the dominant predictor of liver‑related complications. Contemporary non‑invasive risk stratification uses simple serum scores (e.g., Fibrosis‑4 index [FIB‑4]) as a first step, followed by vibration‑controlled transient elastography (VCTE) or liver‑specific blood tests (enhanced liver fibrosis [ELF], procollagen III N‑terminal propeptide [PRO‑C3]) to refine risk and triage referrals. Magnetic resonance elastography (MRE) provides higher accuracy than ultrasound‑based elastography, though limited by its availability. Composite models (Agile 3+/4, steatosis‑associated fibrosis estimator [SAFE]) can reduce indeterminate classifications but may require additional calibration for alcohol exposure for use in MetALD. Prognosis should incorporate dynamic, longitudinal measurement of non‑invasive tests, and integrate extrahepatic risks, particularly cardiovascular and kidney disease. In MetALD, quantitative alcohol assessment can be improved by incorporating objective biomarkers such as phosphatidylethanol (PEth) as part of a triangulation approach that integrates self‑reported alcohol intake, the Alcohol Use Disorders Identification Test‑Consumption (AUDIT‑C), and clinical history.1 Challenges remain, particularly for MetALD, and future research will focus on integrating dynamic biomarkers, predicting treatment response, and utilizing artificial intelligence (AI) for personalized risk and treatment prediction, combining genetic modifiers and polygenic risk scores with clinical and imaging data, with validation across diverse populations.
PMID:42669412 | DOI:10.3350/cmh.2026.0398