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A data-driven framework reconstructs the molecular continuum of human MASLD progression.

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This study introduces a novel framework to track metabolic dysfunction-associated steatotic liver disease (MASLD) progression using molecular data. It identifies a plasma biomarker panel for precise patient staging and predicting advanced fibrosis.

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Area of Science:

  • Hepatology and Molecular Biology
  • Data-driven Disease Modeling
  • Biomarker Discovery

Background:

  • Metabolic dysfunction-associated steatotic liver disease (MASLD) is a progressive condition, but current staging methods are static and fail to capture its continuous nature.
  • Conventional frameworks rely on histology-defined stages, limiting understanding of dynamic disease progression and underlying molecular changes.

Purpose of the Study:

  • To develop a data-driven framework for reconstructing MASLD progression as a continuous molecular trajectory.
  • To identify non-invasive biomarkers for patient stratification and precise disease staging.

Main Methods:

  • Utilized cross-sectional liver transcriptomic profiles to build a molecular trajectory of MASLD progression.
  • Integrated transcriptomic trajectory with liver-plasma proteomics data.
  • Developed and validated a 57-gene plasma biomarker panel.

Main Results:

  • Established a continuous molecular trajectory for MASLD progression, revealing ordered activation of regulatory programs and pathways.
  • Identified a 57-gene plasma biomarker panel that accurately predicts advanced fibrosis.
  • Demonstrated superior performance of the biomarker panel over established clinical scores in independent cohorts for patient positioning along the disease trajectory.

Conclusions:

  • The trajectory-based framework provides a generalizable approach to understanding MASLD pathophysiology.
  • The identified biomarker panel enables precise, non-invasive staging and supports mechanistically informed biomarker discovery.
  • This work lays the foundation for stage-aware therapeutic prioritization in MASLD management.