aceMachine learning-based discrimination of metabolic dysfunction-associated steatotic liver disease: implications of tryptophan metabolites as potential biomarkers
Objectives Early screening of individuals at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) facilitates timely intervention and may reduce the risk of progression to cirrhosis. This study aimed to develop a reliable and clinically practical model for MASLD discrimination. Methods A total of 179 individuals were recruited, including 80 healthy controls and 99 MASLD patients. Non-targeted and targeted metabolomics were performed to identify and validate candidate biomarkers associate…
A Random Forest model combining two tryptophan metabolites (kynurenine and neopterin) with five routine clinical tests discriminated MASLD from healthy controls with high diagnostic performance.
Model developed in a small single cohort of 179 individuals and authors note need for further validation and mechanistic exploration before clinical use.
Evidence
- Peer-reviewedClinica Chimica Acta2026-09-27
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Truvace Impact Record TRV-2026-1223, v1: “Machine learning-based discrimination of metabolic dysfunction-associated steatotic liver disease: implications of tryptophan metabolites as potential biomarkers.” Truvace, 2026-09-30. /record/TRV-2026-1223 (accessed at citation time). sha256 06a542c931d650f4…
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