Novel biomarkers and multi-omics approaches for diagnosis and management of ulcerative colitis

Ulcerative colitis (UC) remains challenging to diagnose, monitor, and treat due to heterogeneous disease presentation and lack of reliable non-invasive markers. Current diagnostic tools, including endoscopy and routine laboratory tests, are limited by invasiveness, cost, and low sensitivity.

This review evaluates emerging biomarkers and multi-omics strategies in UC, highlighting their potential role in disease diagnosis, prognosis, and therapeutic response prediction.

A comprehensive literature analysis was conducted, focusing on faecal, serological, genetic, epigenetic, microbial, and metabolite-based biomarkers, as well as recent advances in transcriptomics, proteomics, and metabolomics. Integration with machine learning approaches was also assessed for their clinical applicability.

Faecal calprotectin and lactoferrin remain reliable non-invasive markers for mucosal inflammation, while serological markers such as CRP and ESR show limited specificity. Genetic variants (IL23R, NOD2) and epigenetic regulators, particularly microRNAs, demonstrate potential for disease stratification. Dysbiosis and altered microbial metabolites further correlate with disease severity and treatment response. Multi-omics integration offers a systems-level view of UC, enabling biomarker panels that improve diagnostic precision and predict therapeutic outcomes. Machine learning tools enhance biomarker-based models, but clinical translation is constrained by variability, validation gaps, and regulatory hurdles.

Emerging biomarkers, especially when integrated across omics platforms and supported by artificial intelligence, provide promising avenues for precision medicine in UC. However, standardisation, external validation, and regulatory qualification remain essential for their successful clinical implementation.