{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:07:24Z","timestamp":1789031244256,"version":"build-2803163510"},"reference-count":161,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    The gut microbiota is essential to maintain host physiology, and its disruption (dysbiosis) is associated with a wide range of diseases. Machine learning (ML) offers a powerful tool to model species-level microbiome profiles, but classifiers that reliably separate healthy from diseased individuals across independent cohorts are still lacking. In this study, we developed a ML classifiers trained on 7,452 publicly available stool metagenomes spanning 32 studies and 12 diseases, designed to distinguish healthy individuals (absence of a clinically diagnosed disease) from non-healthy individuals (presence of a clinically diagnosed disease) based on species-level gut microbiome profiles. We trained 16 supervised models combining four algorithms (RF, SVM-LIN, SVM-RBF, and LR-ElasticNet) combined with all feature sets and three feature-selection algorithms. Performance was assessed by F1 score and ROC\u2013AUC on held-out test data and externally validated on 642 samples from six independent cohorts, including previously unseen diseases. On the test set, all models achieved F1 scores of 78\u201386% and ROC\u2013AUC values of 89\u201395%. An SVM-RBF model using permutation-based feature selection performed best (F1\u202f=\u202f86.6%, ROC\u2013AUC\u202f=\u202f95.5%; healthy F1\u202f=\u202f86.6%, non-healthy F1\u202f=\u202f88.7%). Importantly, external validation confirmed the generalizability of the full-feature SVM-RBF model (overall F1\u202f=\u202f70.6%; ROC\u2013AUC\u202f=\u202f84.7%), including unseen disease types such as\n                    <jats:italic>Clostridioides difficile<\/jats:italic>\n                    infection (F1\u202f=\u202f90.3%) and\n                    <jats:italic>type 2 diabetes<\/jats:italic>\n                    (F1\u202f=\u202f77.4%). Feature-importance and multivariate analyses revealed both shared and disease-specific microbial signatures, suggesting that the model captures biologically meaningful patterns rather than cohort-specific artifacts. Disease-associated taxa included\n                    <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                    ,\n                    <jats:italic>Raoultella ornithinolytica, Sutterella wadsworthensis, Gemmiger formicilis<\/jats:italic>\n                    , and\n                    <jats:italic>Lactobacillus crispatus<\/jats:italic>\n                    . In contrast, healthy status was consistently associated with commensal species such as\n                    <jats:italic>Extibacter hylemonae<\/jats:italic>\n                    and\n                    <jats:italic>Ruthenibacterium lactatiformans<\/jats:italic>\n                    . These results show that models trained on pooled metagenomes predict gut health status accurately and transfer to independent cohorts, providing a scalable, non-invasive framework and a set of candidate microbial biomarkers for further clinical evaluation.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2026.1925500","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:13:50Z","timestamp":1789028030000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data"],"prefix":"10.3389","volume":"17","author":[{"given":"Bablu","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Oncology and Hematology-Oncology, Universit\u00e0 degli Studi di Milano","place":["Milan, Italy"]},{"name":"Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro","place":["Bari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erika","family":"Lorusso","sequence":"additional","affiliation":[{"name":"Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro","place":["Bari, Italy"]},{"name":"National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies","place":["Bari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Fosso","sequence":"additional","affiliation":[{"name":"Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro","place":["Bari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graziano","family":"Pesole","sequence":"additional","affiliation":[{"name":"Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro","place":["Bari, Italy"]},{"name":"National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies","place":["Bari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13059-022-02637-7","article-title":"Meta-analysis defines predominant shared microbial responses in various diseases and a specific inflammatory bowel disease signal","volume":"23","author":"Abbas-Egbariya","year":"2022","journal-title":"Genome Biol."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1038\/s41587-020-0603-3","article-title":"A unified catalog of 204,938 reference genomes from the human gut microbiome","volume":"39","author":"Almeida","year":"2021","journal-title":"Nat. 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