United European Gastroenterology Journal, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 7, 2024
Inflammatory bowel disease (IBD) encompasses a spectrum of immune-mediated disorders the gastrointestinal tract, driven by complex and only partially understood host-microbiome crosstalk.1 Individual host features microbiome composition are central to IBD pathophysiology, influencing phenotype, progression, outcomes. Recent research has demonstrated that gene expression microbiota highly personalised, reflecting tissue specificity, subtype, inflammatory status.1 Navigating this complexity is crucial for identifying novel therapeutic targets understanding mechanisms underlying treatment response, paving way precision medicine in IBD. OMIC techniques, including genomics, transcriptomics, proteomics, metagenomics, high-throughput techniques which enable deep characterisation patients.2 Recently, additional OMICs fields, example, epigenomics, metabolomics, lipidomics, have been introduced, expanding our pathobiology complexity. When applied biological samples from patients, these allow patient-specific disease, uncovering new networks targets, predicting progression outcomes.2 Nonetheless, mainly conducted separately each other. Since arises convergence multiple factors, functional integration multi-OMICs data key fully dissecting disease. The literature on application remains scant, though preliminary findings particularly promising.3 Multi-OMICs approaches recently shed light role gut microbial ecosystems IBD,4 while also elucidating host-microbial molecular networks. Additionally, epigenomic, transcriptomic, metagenomic potential outcomes, need biologic therapy.5 These encouraging results led foundation consortia dedicated leveraging in-depth prediction responses.6 Results multi-centre studies expected improve quality life, minimise patient risk, reduce expenditures ineffective treatment. constrained several critical challenges, specialised expertise analysis interpretation, substantial heterogeneity across studies, overwhelming volume data, difficult humans manage effectively. Artificial intelligence (AI) offers transformative opportunity context. Applying machine learning models big large digital medical datasets can standardise accurately integrate generating deeper more comprehensive insights making manageable practical clinical use. In United European Gastroenterology, Cannarozzi colleagues provide review advancements AI IBD.7 Their underscores early yet promising applications AI-driven research. While initial limited small sample sizes less stringent approaches, compelling. One most exciting developments using AI-enabled characterise host-microbe interactions predict responses therapies accurately. Considering range available resulting decision-making IBD,8 advancement represents significant leap forward medicine. may accurate tailoring individual patients based their unique profiles. This personalised approach promises streamline decision-making, healthcare costs optimising strategies dosages. push boundaries further enabling holistic integrates diverse records, laboratory results, endoscopic findings, histologic features, multi-OMICs. multi-modal integrative approach, known as 'endo-histo-omics',9 profiling, shaping future It holds promise bridge long-standing gaps trials practice, such diagnosis, biomarker discovery, outcome prediction, decisions. pivotal achieving goal.10 models, pre-trained extensive then fine-tuned specific tasks, excel at seamlessly integrating analysing multimodal datasets, potentially expediting implementation 'endo-histo-omics' settings. Despite ongoing challenges implementation, quality, reproducibility, explainability, trustworthiness, privacy, cyber-security, novel, standardised with clinical, histological data. translate innovative into care authors declare no conflict interest related editorial. Data sharing not applicable article were created or analyzed study.
Language: Английский