Artificial intelligence-driven multi-omics analysis of gut-kidney axis in chronic kidney disease

The complex interactions between gut microbiota and kidney function in chronic kidney disease (CKD) present a challenging phenomenon in nephrology research. This comprehensive review explores how artificial intelligence (AI) is utilised for our understanding of the gut-kidney axis through multiomics analysis, offering a new platform for disease management and therapeutic interventions. Recent advances in multi-omics technologies have generated unprecedented volumes of data across microbiomics, metabolomics and proteomics platforms, necessitating sophisticated AI-driven approaches for meaningful interpretation. So, we substantially examined how machine learning methods integrate the omics data to establish the relationship between the gut-kidney axis for a more accurate predictive model and biomarker discovery. In addition, we explained the overview of molecular routes that relate microbiome changes to uremic toxin generation and inflammatory cascades in CKD patients. This timely review offers significant basic insights into using AI to better understand the pathogenesis of CKD progression in early stages via the gut-kidney route.