西方饮食摄入、衰老以及法尼索迪X受体（FXR）的失活是代谢和慢性炎症相关健康问题的危险因素，从代谢功能紊乱相关脂肪性肝病（MASLD）到痴呆。当这些风险相结合时，MASLD的进展可能会加剧。FXR的失活，即胆酸（BA）的受体，容易导致人类和小鼠的癌症。本研究使用多组学技术，包括肝转录组、肝脏、血清和尿液代谢物、肝胆酸以及肠道微生物组，基于机器学习方法对这些风险进行分类。我们采用线性支持向量机和K折交叉验证进行分类和特征选择。我们发现仅增加尿液中的蔗糖就可以达到91%的准确率来预测西方饮食的摄入。肝内沥胆酸和血清丙酮酸的准确率分别为100%和95%，可以用来分类衰老。尿液代谢物（肌酸和牛磺酸减少以及琥珀酸增加）或肠道细菌增加（多雷拉菌、脱卤芽孢杆菌和散斑螺旋菌）可以以超过90%的准确率预测FXR的失活。代谢物-疾病相互作用网络可部分揭示人类疾病的相关性。转录组数据还与人类肝脏疾病数据集进行了比较。减少的Cyp39a1（细胞色素P450家族39亚家族A成员1）和增加的Gramd1b（GRAM结构域含有1B）在人类肝癌和代谢性肝病中也发生了变化。综上所述，我们的数据有助于在肠肝轴中鉴定无创生物标志物来预测代谢状态。

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-disease 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-liver axis to predict metabolic status.