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ABSTRACT: Crohn's disease (CD) is a highly heterogeneous inflammatory bowel disorder. Despite an expanding therapeutic arsenal, treatment selection remains largely empirical, often leading to delayed effective control and suboptimal outcomes. Precision medicine, which tailors therapy to individual patient characteristics, offers a promising alternative. Advances in biomarker discovery are enabling more personalized approaches. Research has moved beyond conventional markers to high-dimensional data from genomics, proteomics, and microbiome studies. Over 200 genetic susceptibility loci have been identified, and dysbiosis is recognized as a key modulator of disease and treatment response. Integrating these multi-omics data is crucial for building predictive models, a task increasingly aided by artificial intelligence and machine learning. However, translating these discoveries into clinical practice faces significant hurdles. These include insufficient validation across diverse populations, methodological heterogeneity, and a lack of real-world evidence. Biomarker performance may vary significantly across genetically diverse patient cohorts, raising concerns that precision medicine tools validated only in homogeneous populations could inadvertently propagate health disparities. It is also important to consider regional contexts, such as the Middle East and Saudi Arabia, where rising IBD incidence due to urbanization and unique genetic architectures may affect biomarker performance and therapy response. This review synthesizes recent progress, outlines the major barriers to implementation, and discusses future directions, such as leveraging digital health technologies and international collaborations, to accelerate the adoption of precision medicine and improve outcomes for CD patients.
Sun et al. (Tue,) studied this question.