ArticleClinical proteomics2021
Urine proteomics identifies biomarkers for diabetic kidney disease at different stages.
Article in Clinical proteomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Mass spectrometry-based urinary proteomic analysis in feline chronic kidney disease.Physiological reports · 2026Article
- Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.Journal of proteome research · 2026Article
- Integrative Analysis on the Urinary Proteome of Diabetic Kidney Disease, with an Emphasis on Extracellular Matrix Proteins.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Advances in the Identification of Novel Urinary Biomarkers for Diabetic Kidney Disease.Journal of diabetes research · 2026Review
- Inhibition of cell surface GRP78 and activated α2M interaction attenuates kidney fibrosis.JCI insight · 2025Article
- Urine Proteomics for Detection of Potential Biomarkers for End-Stage Renal Disease.International journal of molecular sciences · 2025Article
- The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.Proteomics · 2025Review
- Insights into the molecular underpinning of type 2 diabetes complications.Human molecular genetics · 2025Review
- Complement classical and alternative pathway activation contributes to diabetic kidney disease progression: a glomerular proteomics on kidney biopsies.Scientific reports · 2025Article
- Inflammatory mechanisms in diabetic nephropathy: emerging insights and targeted therapeutics.Frontiers in medicine · 2025Review
- Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study.Biology · 2024Article
- Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort.Clinical proteomics · 2024Article
- Recent progress in mass spectrometry-based urinary proteomics.Clinical proteomics · 2024Review
- Review
- Salivary proteomics profiling reveals potential biomarkers for chronic kidney disease: a pilot study.Frontiers in medicine · 2024Article
- Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort.American journal of cancer research · 2024Article
- New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era.Frontiers in immunology · 2024Review
- Expression of urinary exosomal miRNA-615-3p and miRNA-3147 in diabetic kidney disease and their association with inflammation and fibrosis.Renal failure · 2023Article
- Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies.Molecules (Basel, Switzerland) · 2023Article
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36 authors.
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Abstract
backgroundType 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the 'gold standard' for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.
methodsIn this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.
resultsWe quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.
conclusionsOur study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.
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