%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N
%P e65563
%T Risk Stratification in Immunoglobulin A Nephropathy Using Network Biomarkers: Development and Validation Study
%A Tan,Jiaxing
%A Yang,Rongxin
%A Xiao,Liyin
%A Dong,Lingqiu
%A Zhong,Zhengxia
%A Zhou,Ling
%A Qin,Wei
%+ Division of Nephrology, Department of Medicine, West China Hospital of Sichuan University, No 37 Guoxue Alley, Wuhou District, Chengdu, 610041, China, 86 18980602119, qinweihx@scu.edu.cn
%K IgA nephropathy
%K unsupervised learning
%K network biomarker
%K metabolomics
%K gut microbiota
%K biomarkers
%K risk stratification
%K IgA
%K immunoglobulin A
%K renal biopsy
%K renal
%K prospective cohort
%K Berger disease
%K synpharyngitic glomerulonephritis
%K kidney
%K immune system
%K glomerulonephritis
%K kidney inflammation
%K chronic kidney disease
%K renal disease
%K nephropathy
%K nephritis
%D 2025
%7 10.3.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Traditional risk models for immunoglobulin A nephropathy (IgAN), which primarily rely on renal indicators, lack comprehensive assessment and therapeutic guidance, necessitating more refined and integrative approaches. Objective: This study integrated network biomarkers with unsupervised learning clustering (k-means clustering based on network biomarkers [KMN]) to refine risk stratification in IgAN and explore its clinical value. Methods: Involving a multicenter prospective cohort, we analyzed 1460 patients and validated the approach externally with 200 additional patients. Deeper metabolic and microbiomic insights were gained from 2 distinct cohorts: 63 patients underwent ultraperformance liquid chromatography–mass spectrometry, while another 45 underwent fecal 16S RNA sequencing. Our approach used hierarchical clustering and k-means methods, using 3 sets of indicators: demographic and renal indicators, renal and extrarenal indicators, and network biomarkers derived from all indicators. Results: Among 6 clustering methods tested, the KMN scheme was the most effective, accurately reflecting patient severity and prognosis with a prognostic accuracy area under the curve (AUC) of 0.77, achieved solely through cluster grouping without additional indicators. The KMN stratification significantly outperformed the existing International IgA Nephropathy Prediction Tool (AUC of 0.72) and renal function-renal histology grading schemes (AUC of 0.69). Clinically, this stratification facilitated personalized treatment, recommending angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for lower-risk groups and considering immunosuppressive therapy for higher-risk groups. Preliminary findings also indicated a correlation between IgAN progression and alterations in serum metabolites and gut microbiota, although further research is needed to establish causality. Conclusions: The effectiveness and applicability of the KMN scheme indicate its substantial potential for clinical application in IgAN management.
%M 40063072
%R 10.2196/65563
%U https://www.jmir.org/2025/1/e65563
%U https://doi.org/10.2196/65563
%U http://www.ncbi.nlm.nih.gov/pubmed/40063072
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