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

Le résumé fourni par la source

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. 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. This study highlights the promising application of metabolic profiling as a non-invasive tool for precise management of IgAN.

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