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TRUVACE RECORD VERSION
record: TRV-2026-1223
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-30T06:56:34.787147Z
status: published
lens: g_space
sector: health
headline: Machine learning-based discrimination of metabolic dysfunction-associated steatotic liver disease: implications of tryptophan metabolites as potential biomarkers
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: provides a promising tool to identify individuals at high risk of MASLD
problem_reading: (none)
problem_evidence: (none)
quick_read: In a 179-person study of 80 healthy controls and 99 MASLD patients published September 2026, investigators used non-targeted and targeted metabolomics to identify dysregulated tryptophan metabolites and trained seven machine learning classifiers for MASLD discrimination, selecting a Random Forest model that combines kynurenine, neopterin and five routine labs.

The approach matters because early identification of high-risk MASLD could enable timely intervention to reduce progression to cirrhosis, but the reported excellent performance is from a small internal cohort and the authors frame the tool as promising and requiring further validation and mechanistic work.
limitation: Model developed in a small single cohort of 179 individuals and authors note need for further validation and mechanistic exploration before clinical use.
tag: Evidence-backed gain
key_points: Study recruited 179 individuals including 80 healthy controls and 99 MASLD patients. | Non-targeted metabolomics identified tryptophan metabolism as the most significantly enriched pathway in MASLD. | Targeted metabolomics verified serum tryptophan metabolites were significantly dysregulated between groups. | Final model used kynurenine, neopterin, triglyceride, high-density lipoprotein cholesterol, uric acid, alanine aminotransferase, and glucose.
rundown: Researchers performed non-targeted metabolomics to find enriched pathways, then targeted metabolomics to validate tryptophan metabolites, and built seven machine learning algorithms assessed by accuracy, sensitivity, specificity and AUC with Shapley Additive Explanations for interpretation.

The selected Random Forest model integrated kynurenine and neopterin with triglyceride, HDL cholesterol, uric acid, ALT and glucose, achieving AUC 0.926-0.972 across evaluations in the 179-person cohort.
sources:
- peer_reviewed | Clinica Chimica Acta | https://doi.org/10.1016/j.cca.2026.122854 | 2026-09-27
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