{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T03:32:14Z","timestamp":1768275134954,"version":"3.49.0"},"reference-count":114,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01CA179582 and R01CA222490"],"award-info":[{"award-number":["U01CA179582 and R01CA222490"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30AG010129"],"award-info":[{"award-number":["P30AG010129"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["H 18-10925-0 and 22-10079"],"award-info":[{"award-number":["H 18-10925-0 and 22-10079"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH National Institute on Aging Grants","award":["U01CA179582 and R01CA222490"],"award-info":[{"award-number":["U01CA179582 and R01CA222490"]}]},{"name":"NIH National Institute on Aging Grants","award":["P30AG010129"],"award-info":[{"award-number":["P30AG010129"]}]},{"name":"NIH National Institute on Aging Grants","award":["H 18-10925-0 and 22-10079"],"award-info":[{"award-number":["H 18-10925-0 and 22-10079"]}]},{"name":"California Department of Public Health, Chronic Disease Control Branch, Alzheimer\u2019s Disease Program","award":["U01CA179582 and R01CA222490"],"award-info":[{"award-number":["U01CA179582 and R01CA222490"]}]},{"name":"California Department of Public Health, Chronic Disease Control Branch, Alzheimer\u2019s Disease Program","award":["P30AG010129"],"award-info":[{"award-number":["P30AG010129"]}]},{"name":"California Department of Public Health, Chronic Disease Control Branch, Alzheimer\u2019s Disease Program","award":["H 18-10925-0 and 22-10079"],"award-info":[{"award-number":["H 18-10925-0 and 22-10079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>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 K-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 (Dorea, Dehalobacterium, and Oscillospira) could predict FXR deactivation with greater than 90% accuracy. Human disease relevance is partly revealed using the metabolite\u2013disease interaction network. Transcriptomics data were also compared with the human liver disease datasets. WD-reduced hepatic Cyp39a1 (cytochrome P450 family 39 subfamily a member 1) and increased Gramd1b (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\u2013liver axis to predict metabolic status.<\/jats:p>","DOI":"10.3390\/nu15153406","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T10:00:15Z","timestamp":1690797615000},"page":"3406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Uncovering the Gut\u2013Liver Axis Biomarkers for Predicting Metabolic Burden in Mice"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8885-6396","authenticated-orcid":false,"given":"Guiyan","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Medical Pathology, Laboratory Medicine in Sacramento, University of California, Davis, CA 95817, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rex","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahbaz","family":"Rezaei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2243-7759","authenticated-orcid":false,"given":"Yu-Jui Yvonne","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Medical Pathology, Laboratory Medicine in Sacramento, University of California, Davis, CA 95817, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/S0092-8674(00)00062-3","article-title":"Targeted disruption of the nuclear receptor FXR\/BAR impairs bile acid and lipid homeostasis","volume":"102","author":"Sinal","year":"2000","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41575-020-00404-2","article-title":"The role of farnesoid X receptor in metabolic diseases, and gastrointestinal and liver cancer","volume":"18","author":"Sun","year":"2021","journal-title":"Nat. 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