The prevalence of inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn’s disease (CD) is increasing rapidly worldwide, affecting an estimated 6.8 million people. This increase carries a significant economic burden with annual health care costs exceeding $12,000 and $7,000 for patients with CD and UC, respectively. Drug selection based on individual factors can potentially reduce these costs and improve patient outcomes.
Factors related to the Western lifestyle such as urbanization, high animal protein intake, ultra-processed foods and low fiber consumption are associated with the onset of IBD. Gut microbial diversity also plays a key role, with rural communities exhibiting greater microbial richness than urban populations. Understanding these environmental and microbial influences is crucial for developing preventive strategies.
Despite significant scientific advances, the exact causes of UC and CD remain elusive. A complex interplay of genetics, immune dysregulation, altered gut microbiota, and environmental factors contribute to disease development. Current immunosuppressive treatment options require a more personalized approach.
The field of precision medicine offers hope for personalized IBD treatment. We can potentially predict treatment response and optimize therapy selection by analyzing individual genetic, immunological and microbial profiles. This “multiomics” approach, combined with machine learning, holds the key to unlocking new therapeutic targets and improving patient outcomes.
This review takes a deep dive into the genetic, immunological and microbial drivers of IBD, highlighting potential predictive markers of treatment response. We explore the principles of machine-learning-driven bioinformatics and collaborative research, which pave the way for future precision medicine strategies in IBD. By taking a personalized approach, we can unlock a brighter future for patients living with this chronic condition.