{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T10:16:12Z","timestamp":1788171372203,"version":"build-2803163510"},"reference-count":12,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: Inflammatory bowel disease (IBD) is characterised by severe intestinal microbial dysbiosis. Most machine learning diagnostic models built on the longitudinal HMP2 cohort suffer serious data leakage from random sample-level cross-validation splitting, which leads to artificially inflated AUC values. Additionally, incomplete reporting of microbial preprocessing, random forest hyperparameters and multi-dimensional evaluation metrics reduces the reproducibility of existing research. Methods: We re-analysed the public HMP2 (IBDMDB) longitudinal metagenomic dataset containing 130 unique subjects (103 IBD\/27 healthy controls) and 1627 longitudinal faecal samples. Raw 585 species were filtered by a minimum relative abundance of 1 \u00d7 10\u22125 and sample prevalence \u226520%, retaining 89 taxa; all 1135 metabolic pathways were retained. CLR transformation was applied to compositional abundance data. We performed Wilcoxon differential testing with Benjamini\u2013Hochberg FDR correction, alpha\/beta diversity analysis, and three random forest models (filtered species, all FDR-significant pathways, strictly filtered pathways). Critical improvements included subject-ID-stratified 5-fold cross-validation repeated 5 times, within-fold training-set-only feature importance calculation, and class weighting to balance unbalanced IBD\/control samples. PERMANOVA with subject stratification and PERMDISP dispersion test were implemented with 999 fixed-seed permutations. Results: All four alpha diversity indices were significantly lower in IBD patients (all p &lt; 0.0001). Subject-stratified PERMANOVA showed disease status only explained 1.18% of total Bray\u2013Curtis community variance (R2 = 0.0118, p = 1); PERMDISP detected significant group dispersion heterogeneity (p = 0.027). We identified 63 differentially abundant species and 695 perturbed pathways at FDR &lt; 0.05. Canonical butyrate producers Faecalibacterium prausnitzii and Roseburia hominis showed no significant inter-group differences. Bootstrap 1000-resampling AUC 95% CIs indicated moderate classification performance: species model (0.626\u20130.705, mean AUC = 0.665), all-significant-pathway model (0.645\u20130.712, mean AUC = 0.679), strict-pathway model (0.620\u20130.685, mean AUC = 0.654). Alistipes putredinis and peptidoglycan biosynthesis I were the top taxonomic and pathway biomarkers, respectively. Conclusions: This study established a leakage-free machine learning pipeline for longitudinal microbiome cohorts via subject-level cross-validation splitting. The moderate AUC values eliminate false high performance caused by sample leakage, and we provide reliable candidate microbial and metabolic biomarkers for IBD. Restricted by single-cohort internal validation and unadjusted medication confounders, these markers still require independent multi-centre external verification before clinical translation.<\/jats:p>","DOI":"10.3390\/genes17091053","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:21:19Z","timestamp":1788168079000},"page":"1053","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gut Microbiota and Metabolic Pathway Signatures for Inflammatory Bowel Disease Identified via Subject-Stratified Random Forest Based on the Longitudinal HMP2 Cohort"],"prefix":"10.3390","volume":"17","author":[{"given":"Qiupeng","family":"Du","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Beijing First Hospital of Integrated Chinese and Western Medicine, No.13-2 Jintai Road, Chaoyang District, Beijing 100026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Beijing First Hospital of Integrated Chinese and Western Medicine, No.13-2 Jintai Road, Chaoyang District, Beijing 100026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenchen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Beijing First Hospital of Integrated Chinese and Western Medicine, No.13-2 Jintai Road, Chaoyang District, Beijing 100026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Beijing First Hospital of Integrated Chinese and Western Medicine, No.13-2 Jintai Road, Chaoyang District, Beijing 100026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Integrative HMP Research Network Consortium (2019). The Integrative Human Microbiome Project. Nature, 569, 641\u2013648.","DOI":"10.1038\/s41586-019-1238-8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/s41586-019-1237-9","article-title":"Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases","volume":"569","author":"Arze","year":"2019","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1093\/clinchem\/hvad051","article-title":"Fecal Calprotectin in Gastrointestinal Disease","volume":"69","author":"Murray","year":"2023","journal-title":"Clin. Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"fuad039","DOI":"10.1093\/femsre\/fuad039","article-title":"Faecalibacterium: A bacterial genus with promising human health applications","volume":"47","author":"Huillet","year":"2023","journal-title":"FEMS Microbiol. 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Med."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/9\/1053\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:30:15Z","timestamp":1788168615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/9\/1053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":12,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["genes17091053"],"URL":"https:\/\/doi.org\/10.3390\/genes17091053","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}