Machine Learning to Identify Molecular Markers for Metabolic Disease Development Using Mouse Models

Background Aging, Western diet (WD) intake, and bile acid (BA) receptor farnesoid X receptor (FXR) inactivation are risk factors for metabolic disease development including nonalcoholic fatty liver disease (NAFLD) and chronic inflammation-related health issues such as dementia. The progression of the metabolic disease can be escalated when those risks are combined. Inactivation of FXR is cancer prone in both humans and mice. The current study used omics data generated within the gut-liver axis to classify those risks using bioinformatics and machine learning approaches. Methods Different ages (5, 10, and 15 months) of wild-type (WT) and FXR knockout (KO) male mice were fed with either a healthy control diet (CD) or a WD since weaning. Hepatic transcripts, liver, serum, and urine metabolites, hepatic bile acids (BAs), as well as gut microbiota were used for risk prediction. A linear support vector machine with K -fold cross-validation was used for classification and feature selection. Results Increased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid (LCA) and serum pyruvate had 100% and 95% accuracy, respectively to classify age. Association analyses showed hepatic LCA was positively associated with serum concentrations of acetone, a ketone body, and 1,3-dihydroxyacetone (DHA), but negatively correlated with serum pyruvate. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or gut microbiota (increased Dorea, Dehalobacterium , and Oscillospira ) could predict FXR functional status with greater than 90% accuracy. Integrated pathway analyses revealed that the predictors for diet and FXR expression were implicated in the central carbon metabolism in cancer. To assess the translational relevance, mouse hepatic transcripts were crosschecked with human NAFLD and hepatocellular carcinoma (HCC) datasets. WD-affected hepatic Cyp39a1 and Gramd1b expression were associated with human HCC and NAFLD, respectively. The metabolites and diseases interaction analyses uncovered that the identified features are implicated in human metabolic diseases, mental disorders, and cancer. Conclusion The risk prediction using mouse models contributes to the identification of noninvasive biomarkers for early diagnosis of metabolic disease development. ### Competing Interest Statement The authors have declared no competing interest. * Acmsd : aminocarboxymuconate semialdehyde decarboxylase Adam11 : ADAM metallopeptidase domain 11 A4gnt : alpha-1,4-N-acetylglucosaminyltransferase CD : control diet Cdh19 : cadherin 19 Cyp39a1 : cytochrome P450 family 39 subfamily a member 1 Cyp3a11 : cytochrome P450 family 3 subfamily a member Cyp46a1 : cytochrome P450 family 46 subfamily a member 1 Csad : cysteine sulfinic acid decarboxylase DEGs : differentially expressed genes FXR : farnesoid X receptor GEO : Gene Expression Omnibus Gramd1b : GRAM domain containing 1B Hamp2 : hepcidin antimicrobial peptide 2 HCC : hepatocellular carcinoma Hsf2bp : heat shock transcription factor 2 binding protein Kcnj16 : potassium inwardly rectifying channel subfamily J member 16 Loxl4 : lysyl oxidase like 4 ML : machine learning Mfsd2a : major facilitator superfamily domain containing 2A Mmd2 : monocyte to macrophage differentiation associated 2 NAFLD : nonalcoholic fatty liver disease NAFL : nonalcoholic fatty liver NASH : non-alcoholic steatohepatitis Naip2 : NLR family apoptosis inhibitory protein Pclo : piccolo presynaptic cytomatrix protein Pde5a : phosphodiesterase 5A Rec8 : REC8 meiotic recombination protein Rps27rt : Ribosomal Protein S27 SVM : support vector machine Slc39a4 : solute carrier family 39 member 4 Tdg : thymine DNA gycosylase WD : Western diet Zbtb16 : zinc finger and BTB domain containing 16 Zfp677 : zinc finger protein