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Up to 8% of patients with IBD also develop primary sclerosing cholangitis (PSC), characterised by cholestasis and progressive destruction of the biliary tree, resulting in cirrhosis, end-stage liver disease and cholangiocarcinoma. Clinical outcome can currently not be improved through medication, denoting the importance of diagnosis prior to irreversible damage, which requires biomarkers of (early) disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We employed direct infusion mass spectrometry (DI-MS)-based metabolomics on plasma to build predictive, potentially diagnostic models for PSC-IBC and other phenotypes including IBD subtype, stricture and fistula presence and more. We used this dataset to simultaneously investigate aetiology of these phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Samples of 348 IBD patients were included for analysis. The data was analysed using our previously reported tool,\n                      <jats:italic>MetaboShiny<\/jats:italic>\n                      . We built predictive models using Random Forest (RF), and subsequently combined with univariate statistics to rank m\/z features connected to PSC-IBD. This ranking was used to perform\n                      <jats:italic>mummichog<\/jats:italic>\n                      enrichment analysis connected to metabolic and metagenomic changes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The highest performing predictive model differentiated PSC-IBD from PSC. The metabolic signature was enriched in changes to amino acid and vitamin metabolism, alongside changes to the metagenome suggesting decreases in anti-inflammatory microbial species and increases in pro-inflammatory species.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These results demonstrate the potential of DI-MS-based metabolomics with machine learning to create diagnostic models and generate hypotheses on the metabolomic-metagenomic level. Sharing our dataset of patients will enrich future human IBD metabolomics research possibilities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11306-026-02420-w","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:44:33Z","timestamp":1774691073000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Univariate- and machine learning-based plasma metabolite signature differentiates PSC-IBD from IBD and is predicted to be driven by gut microbial changes"],"prefix":"10.1007","volume":"22","author":[{"given":"Joanna C.","family":"Wolthuis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes P. 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