ArticleFrontiers in artificial intelligence2026
The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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