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September 23, 2025Diabetes Obesity and Metabolism

Unveiling urinary diagnostic biomarkers for diabetic kidney disease using metabolomics and machine learning approaches

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Authors

YSYuan SunHLHaiying LiXYXi Yan

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Overview

This analysis reveals seven biomarkers for diabetic kidney disease, highlighting important metabolic pathways.

Key Points

  • Seven biomarkers were identified with strong diagnostic performance for diabetic kidney disease.
  • Metabolomics and machine learning revealed significant pathways impacted in diabetic kidney disease progression.
  • Correlation analysis indicated these biomarkers correlate with clinical indicators, suggesting early diagnostic value.
  • Biotin and taurine metabolism are key mechanisms involved in diabetic kidney disease progression as per this study.

Cite This Study

Sun et al. (2025) studied this question.

synapsesocial.com/papers/68d43ed7713b0b5dfea7e3e0https://doi.org/10.1111/dom.70138
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Metabolomics-Guided Machine Learning Model for Diagnosis and Differential Diagnosis of Diabetic Kidney Disease: A Dual-Center Study2025
  2. 2Artificial intelligence–based diagnosis of diabetic kidney disease using urinary VOC biosensor data2025 · 2 citations
  3. 3Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease2025 · 4 citations
  4. 4Identification of potential biomarkers for diabetic nephropathy via UPLC-MS/MS-based metabolomics2025 · 4 citations
  5. 5Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger‐onset type 2 diabetes2025 · 3 citations