ArticleKidney research and clinical practice2023
Serum and urine metabolomic biomarkers for predicting prognosis in patients with immunoglobulin A nephropathy.
Article in Kidney research and clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.Renal failure · 2026Review
- Evolving therapeutic landscape of immunoglobulin A nephropathy: targeted immune pathways and novel therapies.Kidney research and clinical practice · 2026Article
- Inhibition of soluble epoxide hydrolase ameliorates renal injury in IgA nephropathy by restoring epoxyeicosatrienoic acids.iScience · 2026Article
- Integrating intestinal microbiome and urinary metabolome data to predict secondary infection in critically ill patients.Critical care (London, England) · 2026Article
- Serum and urine metabolic fingerprints enable diagnosis and prognosis for IgA nephropathy.Materials today. Bio · 2025Article
- The evolving understanding of systemic mechanisms in organ-specific IgA nephropathy: a focus on gut-kidney crosstalk.Theranostics · 2025Review
- CKD Urine Metabolomics: Modern Concepts and Approaches.Pathophysiology : the official journal of the International Society for Pathophysiology · 2023Review
- Metabolomics profiling: a potential tool for predicting immunoglobulin A nephropathy progression.Kidney research and clinical practice · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundImmunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification.
methodsWe performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression.
resultsWe observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression.
conclusionThis study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.
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