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

ABSTRACT: 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 k

SUBMITTER: Wang B 

PROVIDER: S-EPMC12595359 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature