Integrative analysis of gut microbiome and treatment outcomes in nephrotic syndrome: the future of AI/ML driven approaches in disease prediction

Nephrotic syndrome (NS) is an immune-mediated kidney disease; it is characterized by colossal proteinuria, low albumin, pitting, and frequent relapses encountered predominantly in children. Although corticosteroids remain the first line of treatment, steroid responsiveness and risks of relapse still remain a major clinical issue. Recent findings suggest that the gut-kidney axis plays a major role in the pathogenesis and therapeutic response in NS. One of the gut microbiota dysbiosis features in NS is the loss of diversity, the disappearance of short-chain fatty acid (SCFA)-producing microbes, particularly Lachnospira and Roseburia butyrate producers, and an increase in potentially pathogenic genera. Reduction of butyrates disrupts regulatory T-cell (Treg) differentiation and alters the Treg/Th17 ratio, promoting inflammation, steroid resistance, and relapse. Others that modify microbial composition include immunosuppressive agents, antibiotics, and diet that influence immune homeostasis and drug metabolism. CYP3A and P-gp are examples of how the microbiota modulates drug transporters and metabolizing enzymes, leading to inter-individual pharmacokinetic differences in tacrolimus and mycophenolate mofetil. The latest developments in Artificial Intelligence and Machine Learning (AI/ML) are enabling the analysis of multi-omics and complex microbiome data in an integrative manner, enabling the prediction and classification of diseases. Predictive microbial and metabolic signatures can be identified using Random Forests, Support Vector Machines, and XGBoost algorithms. Risk stratification can be performed early by combining genomics, transcriptomics, metabolomics, and microbiome profiling, which is possible through the application of AI, and create an individual therapeutic intervention. This review also presents the interdependence among gut microbiota, immune control, and treatment response in NS, and the transformative potential of AI-based multi-omics systems for predicting and treating each disease.