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CHAIN Cohort: Prospective Multicenter Study of MASLD With Serial Liver Stiffness Assessments in Cardiovascular Care.

Liver international : official journal of the International Association for the Study of the Liver·2026
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Integrated Plasma and Tissue Lipid Profiling Demonstrates a Distinctive Metabolic Profile in MAFLD-Associated Non-Cirrhotic Hepatocellular Carcinoma.

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Hepatic and Cardiovascular Outcomes in Primary Biliary Cholangitis With Metabolic-Dysfunction Associated Steatotic Liver Disease.

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The Changing Etiological Landscape of Liver Cancer in the Era of Metabolic Disease.

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Metabolic Divergence Between MASLD and Metabolic Syndrome: Distinct Clinical Phenotypes and Risk Stratification

Mariana M Ramírez-Mejía1,2, Sandra M Barbalho3, Guadalupe Ponciano-Rodríguez4

  • 1Liver Research Unit, Medica Sur Clinic & Foundation, Mexico City, Mexico.

Liver International : Official Journal of the International Association for the Study of the Liver
|August 25, 2025
PubMed
Summary

Metabolic dysfunction-associated steatotic liver disease (MASLD) shows significant patient heterogeneity. Phenotype-based approaches, rather than metabolic syndrome alone, can improve risk stratification for MASLD patients.

Keywords:
MASLDcardiometabolic riskclusteringmetabolic syndromeobesity

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Area of Science:

  • Hepatology
  • Metabolic Disorders
  • Clinical Research

Background:

  • Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally.
  • Metabolic syndrome (MetS) is a traditional indicator but may not fully represent MASLD's risk spectrum.
  • Understanding MASLD's clinical heterogeneity is crucial for effective management.

Purpose of the Study:

  • To explore phenotypic heterogeneity within MASLD.
  • To investigate the overlap and divergence between MASLD and MetS.
  • To improve clinical stratification of MASLD patients.

Main Methods:

  • An unsupervised clustering approach was used on a discovery cohort (n=600).
  • Standardized clinical and metabolic variables were analyzed to identify distinct phenotypes.
  • External validation was performed on an independent cohort (n=407).

Main Results:

  • A two-cluster model identified low-risk and high-risk groups (enriched for MASLD and MetS).
  • A three-cluster model revealed an intermediate group with MASLD but without MetS, showing diverse metabolic features.
  • Cluster structures were validated, and cluster membership correlated with steatosis and fibrosis parameters.

Conclusions:

  • MASLD exhibits significant phenotypic heterogeneity, partially diverging from MetS.
  • MetS alone may not fully capture the metabolic dysfunction spectrum in MASLD.
  • Phenotype-based strategies using clinical and metabolic data can enhance MASLD risk stratification and personalize treatment.