Serum and urine metabolic fingerprints enable diagnosis and prognosis for IgA nephropathy

IgA nephropathy (IgAN), the most common primary glomerulonephritis worldwide, displays a pronounced geographical variation and progresses to end-stage renal disease within 20 years in 20-40 % of patients. However, current clinical management remains challenged by the invasive nature of kidney biopsy. To meet the urgent need for non-invasive strategies, we developed a dual-fluid metabolic profiling approach for early diagnosis and prognostic evaluation. By leveraging nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to obtain metabolic fingerprints from serum and urine, combined with machine learning integration, our model achieved high diagnostic performance (AUC = 0.81-0.95) in distinguishing IgAN from healthy donors. Moreover, we tracked dynamic changes in key metabolites throughout treatment, revealing distinct metabolic pathways among different prognostic subgroups (p < 0.05). This study highlights the promising application of metabolic profiling as a non-invasive tool for precise management of IgAN.