Noninvasive biomarkers implicated in urea and TCA cycles for metabolic liver disease

• Main Authors: Guiyan Yang (Author), Yu-Jui Yvonne Wan (Author)
• Format: Book
• Published: BMC, 2024-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s40364-024-00694-7; 2050-7771
• Keywords: Liver; Metabolic disease; Machine learning; Bile acid; FXR; Gut-liver axis; Therapeutics. Pharmacology; RM1-950
• Source: Biomarker Research, Vol 12, Iss 1, Pp 1-7 (2024)

Abstract Bile acid (BA) and its receptor FXR play crucial roles in metabolism, and dysregulated BA synthesis regulated by hepatic and bacterial enzymes causes metabolic dysfunction-associated steatohepatitis (MASH) and hepatocellular carcinoma (HCC). Moreover, because ~ 75% of hepatic blood is from the gut, liver metabolism is influenced by intestinal bacteria and their metabolites. Thus, we used gut microbiota and metabolites from the urine and serum to uncover biomarkers for metabolic distress caused by Western diet (WD) intake, aging, and FXR inactivity. Hepatic transcriptomes were profiled to define liver phenotypes. There were 654 transcriptomes commonly altered by differential diet intake, ages, and FXR functional status, representing the signatures of liver dysfunction, and 76 of them were differentially expressed in healthy human livers and HCC. Machine learning approaches classified urine and serum metabolites for differential dietary intake and age difference. Additionally, the gut microbiota could predict FXR functional status. Furthermore, FXR was essential for differentiating dietary effects in colonizing age-related gut microbes. The integrated analysis established the relationships between the metabolites and gut microbiota correlated with hepatic transcripts commonly altered by diet, age, and FXR functionality. Remarkably, the changes in metabolites involved in the urea cycle, mitochondrial metabolism, and amino acid metabolism are associated with hepatic dysfunction (i.e. FXF deactivation). Taken together, noninvasive specimens and biomarkers are promising resources for identifying metabolic distress.