ArticleBiomedicines2020
Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease.
Article in Biomedicines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Serum baseline tryptase levels across chronic kidney disease stages: A retrospective report.The journal of allergy and clinical immunology. Global · 2026Article
- Role of Mast Cells and Their Mediators in Chronic Kidney Diseases.International journal of molecular sciences · 2025Review
- Arsenic-induced nephrotoxicity: Mechanisms, biomarkers, and preventive strategies for global health.Veterinary world · 2025Review
- The immune duality of osteopontin and its therapeutic implications for kidney transplantation.Frontiers in immunology · 2025Review
- Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers.International journal of molecular sciences · 2024Review
- Rosuvastatin as a Supplemental Treatment for the Clinical Symptoms of Nephropathia Epidemica: A Pilot Clinical Study.Viruses · 2024Article
- Article
- Identification of kidney injury released circulating osteopontin as causal agent of respiratory failure.Science advances · 2022Article
- High-performance surface-enhanced Raman spectroscopy chip integrated with a micro-optical system for the rapid detection of creatinine in serum.Biomedical optics express · 2021Article
- Healthcare Data for Achieving a More Personalized Treatment of Chronic Kidney Disease.Biomedicines · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).
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Registered trials
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 OpenQuestion graph.