Translating Gut Microbiota into Diagnostics: A Multidimensional Approach for the Diagnosis of Inflammatory Bowel Disease

The gut microbiota has emerged as a key factor in the pathophysiology of inflammatory bowel disease (IBD), providing novel opportunities for diagnostic innovation. Traditional biomarkers, such as C-reactive protein and fecal calprotectin, are widely used in clinical practice; however, their ability to reflect disease complexity and microbial dysregulation remains limited. Recent advances in metagenomics and multi-omics integration have enabled high-resolution profiling of microbial communities and their functional capacities and associated metabolites. Differential abundance analysis and machine learning models have been used to identify microbial biomarkers that can distinguish patients with IBD from healthy individuals. Multicohort studies integrating microbiome and metabolomic data have further improved diagnostic accuracy and generalizability. Transcriptomic and proteomic analyses provide complementary insights into host-microbe interactions and disease mechanisms. In this review, we explored the potential of metagenomic biodata as diagnostic markers for IBD, with an emphasis on a multidimensional analytical approach. We highlight the recent developments in sequencing technologies, computational pipelines for microbial feature selection, and machine learning strategies applied to biomarker discovery. The integration of multi-omics data deepens our understanding of host-microbe interactions and facilitates the development of microbiota-informed diagnostic tools. As multidimensional microbial profiling evolves, its clinical utility for the diagnosis and stratification of IBD requires further investigation.