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June 4, 2026Clinical and Molecular Hepatology1 citationsOpen Access

Precision pathophysiology in steatotic liver disease

WLWonseok LeeDHDa Kyung HwangHKHyun Young Kim

Key Points

  • The aim is to explore the complex phenotypic overlap and distinct molecular features of steatotic liver diseases, specifically MASLD and MetALD.
  • Review of existing literature on metabolic dysfunction and alcohol-associated liver diseases.
  • Examination of multi-omic technologies for understanding disease mechanisms at the cellular level.
  • Discussion of experimental models such as human liver spheroids.
  • Identified substantial phenotypic overlap between MASLD and MetALD.
  • Highlighted the importance of multi-omic insights in understanding liver disease pathology.
  • Proposed that refined models will aid in the development of targeted therapies.

Abstract

Steatotic liver disease (SLD) comprises metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-associated liver disease (MetALD), and alcohol-associated liver disease (ALD), which represent subclasses of liver disorders with overlapping etiologies. MASLD is defined as SLD with cardiometabolic dysfunction, whereas MetALD refers to MASLD with moderate alcohol consumption (140-350 g/week in females and 210-420 g/week in males). Despite being classified as distinct entities, MASLD and MetALD exhibit substantial phenotypic overlap, underscoring the need to delineate their pathological and molecular features and to develop models that capture the synergistic effects of alcohol and metabolic stress. Recent advances in multi-omic technologies have enabled integrated single-cell profiling of genetic, epigenetic, spatial, and proteomic features, providing high-resolution insights into cellular heterogeneity and disease mechanisms. In this review, we examine the pathophysiological landscape of SLD, highlight key distinctions between MASLD and MetALD, and discuss experimental models, including human liver spheroids. These approaches provide deeper insights into disease classification and accelerate the development of targeted therapies.

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Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/6a2115d7d499ed480b16eecahttps://doi.org/10.3350/cmh.2026.0490
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