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We aimed to comprehensively examine the effects of gut metabolites on NAFLD progression. We performed integrative metabolomics (untargeted discovery and targeted validation) analysis of non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and obesity in children. Fecal samples were collected from 75 subjects in the discovery cohort (25 NAFL, 25 NASH, and 25 obese control children) and 145 subjects in an independent validation cohort (53 NAFL, 39 NASH, and 53 obese control children). Among 2,491 metabolites, untargeted metabolomics revealed a complete NAFLD metabolic map containing 318 increased and 123 decreased metabolites. Then, machine learning selected 65 important metabolites that can distinguish the severity of the NAFLD. Furthermore, precision-targeted metabolomics selected 5 novel gut metabolites from 20 typical metabolites. The functionality of candidate metabolites was validated in hepatocyte cell lines. In the end, this study annotated two novel elevated pathogenic metabolites (dodecanoic acid and creatinine) and a relationship between depleted protective gut microbiota (\n                      <jats:italic>Butyricicoccus<\/jats:italic>\n                      and\n                      <jats:italic>Alistipes<\/jats:italic>\n                      ), increased inflammation (IL-1\u03b2), lipid metabolism (TG), and liver function (ALT and AST). This study demonstrates the role of novel gut metabolites (dodecanoic acid and creatinine), as the fatty acid metabolism regulator contributing to NAFLD development through its influence on inflammation and liver function.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>\n                      Altered gut microbiota and metabolites are a major cause of non-alcoholic fatty liver disease (NAFLD) in children. This study demonstrated a complete gut metabolic map of children with NAFLD, containing 318 increased and 123 decreased metabolites by untargeted metabolomic. Multiple validation approaches (machine learning and targeted metabolomic) selected five novel gut metabolites for targeted metabolomics, which can distinguish NAFLD status and severity. The gut microbiota (\n                      <jats:italic>Butyricicoccus<\/jats:italic>\n                      and\n                      <jats:italic>Alistipes<\/jats:italic>\n                      ) and metabolites (creatinine and dodecanoic acid) were novel biomarkers associated with impaired liver function and inflammation and validated by experiments of hepatocyte cell lines. The data provide a better understanding of the importance of gut microbiota and metabolite alterations in NAFLD, which implies that the altered gut microbiota and metabolites may represent a potential target to prevent NAFLD development.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/spectrum.05230-22","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T09:00:29Z","timestamp":1709715629000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":12,"title":["Integrative metabolomics highlights gut microbiota metabolites as novel NAFLD-related candidate biomarkers in children"],"prefix":"10.1128","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1542-9072","authenticated-orcid":true,"given":"Jiayou","family":"Luo","sequence":"first","affiliation":[{"name":"Pediatrics Research Institute of Hunan Province, Hunan Children\u2019s Hospital, Changsha, China"},{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miyang","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China"},{"name":"Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China"},{"name":"Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atipatsa C.","family":"Kaminga","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Mzuzu University, Mzuzu, 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South China, Hengyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yamei","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SiSi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenzhen","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Maternal and Child Health, Xiangya School of Public Health, 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