Stratification of IgA nephropathy using similarity network fusion: clinical, biochemical, and biopsy-based clusters with prognostic implications

IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide, with highly variable clinical manifestations. Risk stratification remains challenging due to disease heterogeneity, despite tools like the International IgA Nephropathy Prediction Tool. Similarity Network Fusion (SNF), a computational approach, integrates multidimensional data, such as clinical, histological, and genetic information, to classify patients into clusters. This study applies SNF to IgAN patients and aims to identify phenotypically distinct clusters, examine their clinical and histological features and evaluate their association with treatment response and kidney survival.