Uncovering the Gut-Liver Axis Biomarkers for Predicting Metabolic Burden in Mice

• Published in: Nutrients 2023-07, Vol.15 (15), p.3406
• Main Authors: Yang, Guiyan, Liu, Rex, Rezaei, Shahbaz, Liu, Xin, Wan, Yu-Jui Yvonne
• Format: Article
• Language: English
• Subjects: Accuracy; Age; Aging; Algorithms; Analysis; Animals; bile acid; Bile acids; Bile Acids and Salts - metabolism; Biological markers; Biomarkers; Biomarkers - metabolism; Cancer; Cholesterol; Classification; cognitive dysfunction; Datasets; Diet; Fatty Liver - metabolism; Feature selection; FXR; Genes; gut–liver axis; Health aspects; Humans; Inflammation; Inflammation - metabolism; Laboratory animals; Liver; Liver - metabolism; Liver cancer; Liver diseases; Liver Neoplasms - metabolism; Machine learning; Metabolic disorders; Metabolites; Mice; Mice, Inbred C57BL; Microbiota; Microbiota (Symbiotic organisms); Oncology, Experimental; Risk factors; Support vector machines; Urine
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu15153406

Western diet (WD) intake, aging, and inactivation of farnesoid X receptor (FXR) are risk factors for metabolic and chronic inflammation-related health issues ranging from metabolic dysfunction-associated steatotic liver disease (MASLD) to dementia. The progression of MASLD can be escalated when those risks are combined. Inactivation of FXR, the receptor for bile acid (BA), is cancer prone in both humans and mice. The current study used multi-omics including hepatic transcripts, liver, serum, and urine metabolites, hepatic BAs, as well as gut microbiota from mouse models to classify those risks using machine learning. A linear support vector machine with -fold cross-validation was used for classification and feature selection. We have identified that increased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid and serum pyruvate had 100% and 95% accuracy, respectively, to classify age. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or increased gut bacteria ( , , and ) could predict FXR deactivation with greater than 90% accuracy. Human disease relevance is partly revealed using the metabolite-disease interaction network. Transcriptomics data were also compared with the human liver disease datasets. WD-reduced hepatic (cytochrome P450 family 39 subfamily a member 1) and increased (GRAM domain containing 1B) were also changed in human liver cancer and metabolic liver disease, respectively. Together, our data contribute to the identification of noninvasive biomarkers within the gut-liver axis to predict metabolic status.