Article Text
Abstract
Eosinophilic oesophagitis (EoE) is a chronic, immune-mediated condition characterised by eosinophilic infiltration of the oesophagus, leading to significant morbidity due to oesophageal dysfunction. The pathogenic course of EoE begins with tissue injury, marked by the intricate interplay of oesophageal barrier dysfunction and T helper 2-mediated inflammation. In response to tissue damage, a subsequent phase of tissue remodelling features a complex interaction between epithelial cells and stromal cells, aimed at tissue repair. The persistence of inflammation drives these mechanisms towards oesophageal fibrostenosis, mainly through the transforming growth factor-dependent, myofibroblast-driven accumulation of the extracellular matrix. Currently, treatment options for EoE are limited, with dietary intervention, proton pump inhibitors and oral steroids serving as first-line therapies. Dupilumab, an antiinterleukin (IL) 4/IL-13 agent, is the only biologic that has been approved by European and American regulatory authorities. However, emerging OMIC technologies significantly advance our understanding of EoE pathogenesis, revealing novel cellular and molecular mechanisms driving the disease. This progress has accelerated the identification of new therapeutic targets and agents, some already under clinical investigation, potentially expanding our therapeutic arsenal and paving the way for more personalised approaches. In this evolving landscape, artificial intelligence (AI) has shown great potential to further elaborate on the complexities of EoE heterogeneity, offering standardised tools for diagnosis, disease phenotyping, and prediction of treatment response. Though still in their early stages, integrating OMICs and AI marks a pivotal step towards precision medicine in EoE.
- OESOPHAGEAL DISORDERS
- MOLECULAR MECHANISMS
- CLINICAL TRIALS
- MACHINE LEARNING
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Footnotes
Contributors Conceptualisation: GS, SG, MI, ADS; writing—original draft preparation: GS, CMR, MVL; writing—review and editing: GS, SG, MI, ADS; supervision: SG, MI, ADS. All authors have read and agreed to the published version of the manuscript. ADS is the guarantor of the article.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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