ReviewBiochemia medica2023
Diagnostic challenges of diabetic kidney disease.
Review in Biochemia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Identification of diagnostic markers for diabetic kidney disease by weighted gene co‑expression network analysis and machine learning.International journal of molecular medicine · 2026Article
- Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Humans.Diabetes & metabolism journal · 2026Article
- Development and validation of an early risk prediction model based on inflammatory and metabolically derived markers for early diagnosis of diabetic kidney disease.Scientific reports · 2026Article
- Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.Journal of proteome research · 2026Article
- Metabolomics for Preclinical Detection of Diabetic Kidney Disease: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study.Frontiers in endocrinology · 2026Article
- The Role and Clinical Significance of miR-484 in the Regulation of SGLT2 in Diabetic Nephropathy.International journal of endocrinology · 2026Article
- Clinical Utility of Urinary Cystatin C in Early Screening and Staging of Diabetic Kidney Disease in Type 2 Diabetes.International journal of endocrinology · 2026Article
- Integration of digital physicalomics and dual-fluid metabolomics flux ratios reveals tubular secretory dysfunction in early diabetic kidney disease.Frontiers in endocrinology · 2026Article
- Animal models for diabetic kidney disease: perspectives and prospects.Frontiers in veterinary science · 2026Article
- The multifunctional role of bovine colostrum in managing diabetes: clinical insights and potential therapeutic effects.Journal of diabetes and metabolic disorders · 2025Review
- Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.Renal failure · 2025Article
- ABCDE to identify and prevent chronic kidney disease: a call to action.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Article
- Associations between serum JAML, nesfatin-1, and 25(OH)D and the risk of diabetic kidney disease in patients with type 2 diabetes.Scientific reports · 2025Article
- Preliminary insight into the potential role of Leptin Receptor Polymorphisms in Type 2 Diabetes Risk: case-control study and bioinformatics analysis.Journal of diabetes and metabolic disorders · 2025Article
- A nomograph model for predicting the risk of diabetes nephropathy.International urology and nephrology · 2025Article
- Implementing novel complete blood count-derived inflammatory indices in the diabetic kidney diseases diagnostic models.Journal of diabetes and metabolic disorders · 2025Article
- The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease.Scientific reports · 2025Article
- Urobiome of patients with diabetic kidney disease in different stages is revealed by 2bRAD-M.Journal of translational medicine · 2025Article
- Urinary Fetuin-A with Specific Post-Translational Modification in Type 1 Diabetes Patients with Normoalbuminuria and Preserved Kidney Function.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal disease (ESRD). It has been evidenced that targeted interventions at an early stage of DKD can efficiently prevent or delay the progression of kidney failure and improve patient outcomes. Therefore, regular screening for DKD has become one of the fundamental principles of diabetes care. Long-established biomarkers such as serum-creatinine-based estimates of glomerular filtration rate and albuminuria are currently the cornerstone of diagnosis and risk stratification in routine clinical practice. However, their immanent biological limitations and analytical variations may influence the clinical interpretation of the results. Recently proposed new predictive equations without the variable of race, together with the evidence on better accuracy of combined serum creatinine and cystatin C equations, and both race- and sex-free cystatin C-based equation, have enabled an improvement in the detection of DKD, but also require the harmonization of the recommended laboratory tests, wider availability of cystatin C testing and specific approach in various populations. Considering the complex pathophysiology of DKD, particularly in type 2 diabetes, a panel of biomarkers is needed to classify patients in terms of the rate of disease progression and/or response to specific interventions. With a personalized approach to diagnosis and treatment, in the future, it will be possible to respond to DKD better and enable improved outcomes for numerous patients worldwide.
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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 Socratic graph.