{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:36:52Z","timestamp":1791308212718,"version":"4.3.1"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Untargeted metabolomics, anchored in high-resolution mass spectrometry, has matured into the central analytical platform of human exposomics. It can capture endogenous biology, diet, drugs, microbial chemistry, environmental contaminants, and their transformation products from a single biological sample. Yet exposome science remains stubbornly single-study: most untargeted exposomics publications stand alone, featuring tables, partial annotations, and semi-quantitative intensities that cannot be combined across cohorts. The bottleneck is no longer instrumentation or annotation; it is interoperability. Existing standards, including MSI, mQACC, BP4NTA, NORMAN, MERIT, mzML, mzTab-M, ISA-Tab, the Universal Spectrum Identifier, RefMet, ChEBI, MetaboLights, Metabolomics Workbench, GNPS\/MassIVE, and the emerging GA4GH human exposome data standards, cover the necessary ingredients but do not yet compose a single, executable profile. I argue that the next stage of exposomics must move from FAIR deposition to meta-analysis-ready evidence: a four-layer stack of acquisition comparability, machine-readable reporting, evidence-aware annotation, and standardized summary statistics, validated by a living community benchmark. Cumulative exposome science depends on it.<\/jats:p>","DOI":"10.3389\/frai.2026.1901969","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T05:30:29Z","timestamp":1787549429000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics"],"prefix":"10.3389","volume":"9","author":[{"given":"Thomas","family":"Hartung","sequence":"first","affiliation":[{"name":"Doerenkamp-Zbinden Chair for Evidence-based Toxicology, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Center for Alternatives to Animal Testing (CAAT)","place":["Baltimore, MD, United States"]},{"name":"CAAT Europe, University of Konstanz","place":["Konstanz, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1001\/jama.2025.8666","article-title":"Beyond genes: human exposome project to tackle external drivers of disease","volume":"334","author":"Anderer","year":"2025","journal-title":"JAMA"},{"key":"B2","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1002\/etc.34","article-title":"Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment","volume":"29","author":"Ankley","year":"2010","journal-title":"Environ. 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