Emerging concepts in the diagnosis and management of metabolically associated steatotic liver disease
- PMID: 41076573
- DOI: 10.1097/MED.0000000000000935
Emerging concepts in the diagnosis and management of metabolically associated steatotic liver disease
Abstract
Purpose of review: Given the global rise of MASLD, which impacts approximately one-third of the population, there is a need for earlier diagnosis and effective treatment strategies to avoid long-term hepatic cardiovascular and renal complications. This review summarizes the recent advances in noninvasive diagnosis and new pharmacological agents approved for MASLD.
Recent findings: The main step forward in diagnostics is a step away from invasive biopsy and emphasis on noninvasive methods including serum biomarkers (e.g. CK-18 and FGF21), imaging (e.g. MRI-PDFF and US-FLI), combination of the two and use of artificial intelligence and machine learning models, for early detection and risk stratification of MASLD and MASH. Multiomics approaches, such as metabolomics and lipidomics, reveal disease-specific signatures, and may help with phenotypic classification of MASLD. Personalized management for MASLD include gut microbiota modulation and point-of-care devices for rapid diagnosis. Novel therapies include THR β agonists, GLP-1/dual GLP-1/GIP agonists, FXR agonists and FGF analogues, which show promise in reducing hepatic fat and fibrosis.
Summary: These findings enable earlier MASLD diagnosis and tailored interventions, improving clinical outcomes in primary care and resource-limited settings. Future research should focus on validating cost-effective tools, and developing combination therapies to address the multifaceted nature of MASLD.
Keywords: machine-learning; metabolic dysfunction-associated steatotic liver disease; metabolomics; noninvasive diagnostics; therapeutics.
Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.
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