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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

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Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.

Jingru Song¹, Ziwei Gao², Liqun Lai¹

¹Department of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, Zhejiang, China.

BMC Gastroenterology

|February 6, 2025

Summary

Serum metabolomics, particularly using proton nuclear magnetic resonance (1H-NMR), significantly enhances the prediction of cirrhosis risk in chronic liver disease patients. Integrating these metabolic profiles with existing scores like APRI and FIB-4 improves risk stratification for early screening.

Keywords:

Chronic liver disease Cirrhosis Elastic net regularization Metabolomics Risk stratification

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Area of Science:

Biochemistry
Medical Diagnostics
Genomics & Proteomics

Background:

Cirrhosis is a major cause of mortality in chronic liver disease (CLD).
Metabolomic technologies capture metabolic shifts during cirrhosis progression.

Purpose of the Study:

To investigate the role of serum metabolomics in stratifying cirrhosis risk among CLD patients.
To assess if integrating metabolomic data with clinical scores improves cirrhosis prediction.

Main Methods:

Utilized 1H-NMR serum metabolomics data from the UK Biobank.
Employed elastic net-regularized Cox proportional hazards models.
Integrated metabolomics with APRI and FIB-4 scores for predictive modeling.

Main Results:

Several metabolites independently associated with cirrhosis events were identified.
Integrating metabolomics with FIB-4 improved predictive performance (ΔC=0.021, NRI=0.504).
Combining metabolomics with APRI also enhanced prediction (ΔC=0.029, NRI=0.378), highlighting key metabolites in lipid and amino acid metabolism.

Conclusions:

1H-NMR serum metabolomics significantly improves cirrhosis risk prediction in CLD patients.
The APRI + Metabolomics model shows strong discriminatory power.
Metabolites involved in fatty acid and amino acid metabolism are key predictors for early cirrhosis risk screening.