Mining biomarkers for type 2 diabetic nephropathy based on urinary proteomics and metabolomics
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By
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Mindong Mi
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Tianhuan Xiong
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Jiyong Gong
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Weijie Sun
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Tunguang Xu
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Qifeng Jiang
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Danqing Zhang
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Junge Zhang
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Jiancheng Huang
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Wei Liang
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June 22, 2026
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Clinical Scorecard: Identifying Urinary Biomarkers for Diabetic Nephropathy in Type 2 Diabetes through Proteomic and Metabolomic Analysis
At a Glance
| Category | Detail |
|---|
| Condition | Diabetic Kidney Disease (DKD) |
| Key Mechanisms | Urinary biomarkers for early diagnosis and staging of DKD using proteomic and metabolomic analysis. |
| Target Population | Patients with type 2 diabetes and healthy controls. |
| Care Setting | Clinical research for biomarker identification. |
Key Highlights
- UTRF and UIgG showed excellent diagnostic value (AUC 0.926 and 0.916, respectively).
- A combination of 20 urinary amino acids demonstrated outstanding diagnostic value (AUC 0.928).
- SERPINA1 identified as a highly promising DKD biomarker with AUC 0.964.
- Current diagnostic methods have significant limitations, necessitating novel biomarkers.
- The study utilized a multi-dimensional biomarker screening system.
Guideline-Based Recommendations
Diagnosis
- Current clinical diagnosis relies on eGFR and UACR, with microalbuminuria detection being key.
Management
- Novel biomarkers may enhance early diagnosis and management strategies for DKD.
Monitoring & Follow-up
- Regular assessment of urinary biomarkers may improve monitoring of DKD progression.
Risks
- Structural renal injury can occur before microalbuminuria appears, leading to false diagnoses.
Patient & Prescribing Data
200 participants including healthy controls and type 2 diabetes patients.
Integration of proteomics and metabolomics may lead to personalized nephrology.
Clinical Best Practices
- Utilize a multi-dimensional approach for biomarker discovery.
- Incorporate both traditional and novel urinary biomarkers in clinical practice.
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