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This study aims to characterize the microbial alterations in pediatric CD and develop a robust classification model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A total of 1175 fecal metagenomic sequencing samples, predominantly from 3 cohorts of pediatric CD patients, were re-analyzed from raw sequencing data using uniform process pipelines to obtain multidimensional microbial alterations in pediatric CD, including taxonomic profiles, functional profiles, and multi-type genetic variants. Random forest algorithms were used to construct classification models after comparing multiple machine learning algorithms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We found pediatric CD samples exhibited reduced microbial diversity and unique microbial characteristics. Pronounced abundance differences in 45 species and 1357 KEGG orthology genes. Particularly, Enterocloster bolteae emerged as a pivotal pediatric CD-associated species. Additionally, we identified a vast amount of microbial genetic variants linked to pediatric CD, including 192 structural variants, 1256 insertions\/deletions (InDels), and 3567 single nucleotide variants, with a considerable portion of these variants located in non-genic regions. The InDel-based model outperformed other predictive models against multidimensional microbial signatures, achieving an area under the ROC curve (AUC) of 0.982. The robustness and disease specificity were further confirmed in an independent CD cohort (AUC\u2005=\u20050.996) and 5 other microbiome-associated pediatric cohorts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Our study provided a comprehensive landscape of microbial alterations in pediatric CD and introduced a highly effective diagnostic model rooted in microbial InDels, which contributes to the development of noninvasive diagnostic tools and targeted therapies.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ecco-jcc\/jjaf039","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T12:05:20Z","timestamp":1741349120000},"source":"Crossref","is-referenced-by-count":4,"title":["Multimodal metagenomic analysis reveals microbial InDels as superior biomarkers for pediatric Crohn\u2019s disease"],"prefix":"10.1093","volume":"19","author":[{"given":"Mengping","family":"Shen","sequence":"first","affiliation":[{"name":"Putuo People\u2019s Hospital, School of Life Sciences and Technology, Tongji University , Shanghai ,","place":["P. 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Zhao","sequence":"additional","affiliation":[{"name":"Putuo People\u2019s Hospital, School of Life Sciences and Technology, Tongji University , Shanghai ,","place":["P. R. China"]},{"name":"Research Institute, GloriousMed Clinical Laboratory Co, Ltd , Shanghai ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Jiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Genetic Engineering, Fudan Microbiome Center, School of Life Sciences, Fudan University , Shanghai ,","place":["P. R. 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