Evidence mapPaperPMID 41194064Full record

ArticleBMC nephrology2025

Development and validation of a single-nephron estimated glomerular filtration rate model to predict disease progression in IgA nephropathy.

Chen Yang, Shuang Liang, Zhi-Yu Duan, Shu-Wei Duan, Jie Wu, Zhe Feng, Pu Chen, Xiang-Mei Chen, Yong Wang, Guang-Yan Cai

Abstract readValidation Study
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Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chen YangSchool of Medicine, Nankai University, Tianjin, China.
Shuang LiangDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Zhi-Yu DuanDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Shu-Wei DuanDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Jie WuDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Zhe FengDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Pu ChenDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Xiang-Mei ChenDepartment of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing, China.
Yong WangSchool of Medicine, Nankai University, Tianjin, China. wangyong301@263.net.
Guang-Yan CaiSchool of Medicine, Nankai University, Tianjin, China. caiguangyan@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe highly variable clinical progression of IgA nephropathy (IgAN) makes it challenging to accurately predict the risk of disease deterioration in patients. Although elevated single-nephron estimated glomerular filtration rate (eGFR) is implicated in disease progression, its prognostic utility remains underexplored due to methodological limitations in nephron quantification. This study aims to fill this research gap by establishing single-nephron eGFR as a prognostic factor and developing a predictive nomogram for assessing kidney disease progression in IgAN patients.

methodsWe included 190 patients with biopsy-proven IgAN undergoing kidney biopsy and CT imaging. Single-nephron eGFR was calculated by dividing eGFR by nephron number, derived from cortical volume and glomerular density. A Cox model incorporating clinical, pathological, and single-nephron eGFR parameters was developed (training cohort: n = 133) and validated (validation cohort: n = 57). Kidney function decline was defined as an annual eGFR decrease ≥5 ml/min/1·73 m2, ≥40% eGFR reduction, or end-stage renal disease. Model performance was assessed using Harrell’s C-index, time-dependent AUC, and calibration curves.

resultsOf the 410 patients screened, 190 (46%) met the eligibility criteria. The cohort was 55% male patients, with a median age of 36 years (IQR, 30–47). The median follow-up duration was 37 months (IQR, 22–48). The nomogram included smoking history, eGFR, use of calcium channel blockers, and single-nephron eGFR. It demonstrated good discrimination (C-index 0.76 [0.69–0.82] in training; 0.75 [0.65–0.85] in validation) with good calibration. Low-risk patients had significantly longer survival compared to high-risk patients in both the development (P < 0.001) and validation (P = 0.04) cohorts. An interactive Shiny app was developed for clinical use (https://yangchenkay.shinyapps.io/IgAN/).

conclusionsThis study develops a clinically applicable prediction model for IgAN that underscores the prognostic value of single-nephron eGFR in renal outcome and enables effective risk stratification of kidney function decline in patients with IgAN. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Glomerular Filtration RateGlomerulonephritis, IGAAdultDisease ProgressionFemaleHumansMaleMiddle AgedNomogramsPrognosisIgA nephropathyNomogramPrognosisRisk factorsSingle-nephron eGFR

Identifiers

PMID41194064
PMCPMC12590695

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.