Early-life microbiome trajectories as biomarkers to predict health outcomes

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dc.contributor.authorJoos, Raphaela
dc.contributor.authorLavelle, Aonghus
dc.contributor.authorDempsey, Eugene
dc.contributor.authorStanton, Catherine
dc.contributor.authorRoss, Paul
dc.date.accessioned2026-09-09T12:26:01Z
dc.date.available2026-09-09T12:26:01Z
dc.date.issued2026-06-23
dc.description.abstractThe early-life gut microbiome is tightly linked to different aspects of infant development. Microbial colonisation patterns have been repeatedly shown to play a role in a variety of paediatric outcomes, ranging from metabolism and immune function to neurodevelopment. Concomitantly, the identification of early-life biomarkers is crucial, especially considering that for various conditions, reliable diagnostic tools only emerge in early childhood. As such, microbiome data collected in the first two years of life may offer valuable prospects for early detection, prevention, quantification or even correction of adverse health trajectories. With the increasing availability of high-resolution microbiome data, researchers are leveraging both traditional statistical approaches and machine learning (ML) methods to analyse the evolution of these complex microbial communities. While statistical models are well-suited for identifying associations between microbiome features and health states, ML methods allow for predicting health outcomes from those features. This review explores the role of the early-life gut microbiome in infant health and development, with a focus on how data acquisition and analytical methods can shape current knowledge. We contrast statistical approaches with ML methods, summarising key findings on microbial succession and factors influencing it. By addressing current challenges and identifying areas for methodological refinement, we aim to discuss the potential of the microbiome in the assessment of current and future health states of an individual and aid in the development of more robust, clinically-relevant models for paediatric care.en
dc.description.versionPublished Version
dc.format.extent21
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid15
dc.identifier.authororcidJoos, Raphaela
dc.identifier.authororcidLavelle, Aonghus
dc.identifier.authororcidDempsey, Eugene§0000-0002-6266-3462
dc.identifier.authororcidStanton, Catherine
dc.identifier.authororcidRoss, Paul§0000-0003-4876-8839
dc.identifier.citationJoos, R, Lavelle, A, Dempsey, E, Stanton, C & Ross, P 2026, 'Early-life microbiome trajectories as biomarkers to predict health outcomes', Microbiome Research Reports, vol. 5, no. 2, 15, pp. 1-21. https://doi.org/10.20517/mrr.2026.03
dc.identifier.doi10.20517/mrr.2026.03
dc.identifier.endpage21
dc.identifier.issn2771-5965
dc.identifier.issued2
dc.identifier.otherORCID: /0000-0003-4876-8839/work/226277022
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/19210
dc.identifier.volume5
dc.language.isoen
dc.publisherOAE Publishing Inc.
dc.relation.urihttps://www.scopus.com/pages/publications/105042600167
dc.rights© 2026, the Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectEarly-life microbiome
dc.subjectHealthy trajectories
dc.subjectInfant development
dc.subjectMachine learning
dc.subjectMicrobial succession
dc.subjectPaediatric health
dc.subject[Medicine]
dc.subject[INFANT]
dc.subject[APCMicrobiome]
dc.subject[Microbiology]
dc.titleEarly-life microbiome trajectories as biomarkers to predict health outcomesen
dc.typeReview
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