Multi-omics and machine learning poised to advance precision medicine in ulcerative colitis
核心洞察
A comprehensive review highlights emerging biomarkers and multi-omics strategies as promising tools for diagnosing, monitoring, and predicting treatment response in ulcerative colitis (搜索).
Faecal calprotectin (搜索) and lactoferrin remain reliable non-invasive markers of mucosal inflammation, while serological markers such as CRP and ESR show limited specificity.
Genetic variants (IL23R (搜索), NOD2) and microRNAs demonstrate potential for patient stratification, with dysbiosis and altered microbial metabolites correlating with disease severity and treatment response.
Ulcerative colitis (搜索) (UC) remains challenging to diagnose, monitor, and treat due to heterogeneous disease presentation and a lack of reliable non-invasive markers. Current diagnostic tools, including endoscopy and routine laboratory tests, are limited by invasiveness, cost, and low sensitivity. A new review published in Gastroenterología y Hepatología (English Edition) evaluates emerging biomarkers and multi-omics strategies in UC, highlighting their potential role in disease diagnosis, prognosis, and therapeutic response prediction.
Non-Invasive Markers of Mucosal Inflammation
The review finds that faecal calprotectin (搜索) and lactoferrin remain reliable non-invasive markers for mucosal inflammation. In contrast, serological markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) show limited specificity, underscoring the need for more precise tools in clinical practice.
Genetic, Epigenetic, and Microbial Biomarkers
Beyond conventional markers, the analysis identifies genetic variants—including IL23R (搜索) and NOD2—and epigenetic regulators, particularly microRNAs, as demonstrating potential for disease stratification. Dysbiosis and altered microbial metabolites further correlate with disease severity and treatment response, suggesting that a systems-level view of UC may improve diagnostic precision and predict therapeutic outcomes.
Multi-Omics Integration and Machine Learning
Multi-omics integration, spanning transcriptomics, proteomics, and metabolomics, offers a systems-level view of UC that enables biomarker panels capable of improving diagnostic precision and predicting therapeutic outcomes. Machine learning tools enhance these biomarker-based models, yet clinical translation is constrained by variability, validation gaps, and regulatory hurdles.
Path Toward Clinical Implementation
The review concludes that 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.