Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH - Scorecard - MDSpire
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Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH
Clinical Scorecard: Combining Multi-Omics Approaches with Machine Learning to Unravel Cellular Diversity and Fibrotic Regulatory Pathways in the Transition from MASLD to MASH
At a Glance
Category
Detail
Condition
Progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH)
Key Mechanisms
Enrichment and spatial co-localization of monocytes/macrophages and hepatic stellate cells; identification of DTNA+ macrophage subpopulation with M2 polarization, hypoxia, and inflammatory signaling; RUNX2-PLG-PARD3 axis mediating macrophage-HSC interaction contributing to fibrosis
Target Population
Patients with MASLD at risk of progression to MASH
Care Setting
Hepatology and liver disease clinical and research settings
Key Highlights
Identification of a DTNA+ macrophage subpopulation enriched specifically in MASH with pro-fibrotic characteristics
RUNX2 transcription factor as a key regulator driving macrophage differentiation and fibrotic signaling
Machine learning models identified DTNA as an optimal biomarker to distinguish MASLD from MASH non-invasively
Guideline-Based Recommendations
Diagnosis
Utilize DTNA biomarker identified by machine learning models for non-invasive differentiation of MASLD versus MASH
Management
Target the RUNX2–PLG–PARD3 signaling axis between DTNA+ macrophages and hepatic stellate cells to potentially mitigate liver fibrosis progression
Monitoring & Follow-up
Monitor macrophage and hepatic stellate cell activity and spatial interactions as indicators of disease progression
Risks
Progression from MASLD to MASH increases risk of cirrhosis and hepatocellular carcinoma
Patient & Prescribing Data
Individuals with metabolic dysfunction-associated steatotic liver disease progressing to steatohepatitis
Emerging therapeutic targets include modulation of DTNA+ macrophages and RUNX2-mediated pathways to prevent fibrosis
Clinical Best Practices
Incorporate multi-omics and machine learning approaches for precise molecular characterization of liver disease stages
Focus on cellular microenvironment remodeling, especially macrophage and hepatic stellate cell interactions, in disease assessment
Use non-invasive biomarkers such as DTNA to improve diagnosis and guide therapeutic decisions
