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This study aimed to discover novel biomarkers of childhood simple obesity through integrative multi-omics analysis, uncovering their potential connections and providing fresh research directions for the complex pathogenesis and treatment strategies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Transcriptome, untargeted metabolome, and 16\u00a0S rDNA sequencing were conducted on subjects to examine transcripts, metabolites in blood, and gut microflora in stool.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Transcriptomic analysis identified 599 differentially expressed genes (DEGs), of which 25 were immune-related genes, and participated in immune pathways such as antimicrobial peptides, neutrophil degranulation, and interferons. The optimal random forest model based on these genes exhibited an AUC of 0.844. The metabolomic analysis examined 71 differentially expressed metabolites (DEMs), including 12 immune-related metabolites. Notably, lauric acid showed an extremely strong positive correlation with BMI and showed a good discriminative power for obesity (AUC\u2009=\u20090.82). DEMs were found to be significantly enriched in four metabolic pathways, namely \u201cAminoacyl-tRNA biosynthesis\u201d, \u201cValine leucine and isoleucine biosynthesis, and Glycine\u201d, \u201cSerine and threonine metabolism\u201d, and \u201cBiosynthesis of unsaturated fatty acids\u201d. Microbiome analysis revealed 12 differential gut microbiotas (DGMs) at the phylum and genus levels, with <jats:italic>p_Firmicutes<\/jats:italic> dominating in the obese group and <jats:italic>g_Escherichia-Shigella<\/jats:italic> in the normal group. Subsequently, a Random Forest model was developed based on the DEMs, immune-related DEGs, and metabolites with an AUC value of 0.912. The 14 indicators identified by this model could potentially serve as a set of biomarkers for obesity. The analysis of the inter-omics correlation network found 233 pairs of significant correlations. DEGs BPIFA1, BPI, and SAA1, DEMs Dimethy(tetradecyl)amine, Deoxycholic acid, Pathalic anhydride, and DL-Alanine, and DGMs <jats:italic>g_Intestinimonas<\/jats:italic> and <jats:italic>g_Turicibacter<\/jats:italic> showed strong connectivity within the network, constituting a large proportion of interactions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This study presents the first comprehensive description of the multi-omics characteristics of childhood simple obesity, recognizing promising biomarkers. Immune-related markers offer a new perspective for researching the immunological mechanisms underlying obesity and its associated complications. The revealed interactions among these biomarkers contribute to a deeper understanding the intricate biological regulatory networks associated with obesity.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13578-024-01322-5","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T16:09:10Z","timestamp":1732810150000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-omics landscape of childhood simple obesity: novel insights into pathogenesis and biomarkers discovery"],"prefix":"10.1186","volume":"14","author":[{"given":"Yi","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufen","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"HaiDan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XiaoYan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0322-1664","authenticated-orcid":false,"given":"Liqun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"issue":"1","key":"1322_CR1","doi-asserted-by":"publisher","first-page":"214","DOI":"10.3390\/nu14010214","volume":"14","author":"L Szczerbinski","year":"2022","unstructured":"Szczerbinski L, Wojciechowska G, Olichwier A, Taylor MA, Puchta U, Konopka P, et al. 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