ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2024
Urinary peptide analysis to predict the response to blood pressure medication.
Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2024. 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, 12 citations in OpenAlex.
- Protocol for Developing and Validating a Multimarker-Clinical Prediction Model of SGLT2 Inhibitor-Induced Acute eGFR Dip in CKD Stages 3-4: A Three-Stage Urinary Proteomics Study.Life (Basel, Switzerland) · 2026Article
- Recent Developments and Applications of Capillary and Microchip Electrophoresis in Proteomics and Peptidomics (2023-2025).Journal of separation science · 2026Review
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Recent Developments in Capillary and Microchip Electroseparations of Peptides (2023-mid 2025).Electrophoresis · 2026Review
- Urine as a source of biomarkers and biological knowledge in chronic kidney disease.Nature reviews. Nephrology · 2026Review
- In silico prediction of optimal multifactorial intervention in chronic kidney disease.Journal of translational medicine · 2025Article
- Article
- The Future of Chronic Kidney Disease Treatment: Combination Therapy (Polypill) or Biomarker-Guided Personalized Intervention?Biomolecules · 2025Review
- Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers.International journal of molecular sciences · 2024Review
- Chronic kidney disease in type 1 diabetes: translation of novel type 2 diabetes therapeutics to individuals with type 1 diabetes.Diabetologia · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 4 countries.
Funding
Abstract
backgroundThe risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression.
methodsWe evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi.
resultsThe classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts.
conclusionsThe DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.
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