Unveiling urinary diagnostic biomarkers for diabetic kidney disease using metabolomics and machine learning approaches
Authors' conclusion
This study initially identified seven DKD biomarkers with excellent diagnostic performance and further clarified their potential role in mediating DKD progression via mechanisms involving biotin metabolism, taurine and hypotaurine metabolism and steroid hormone biosynthesis. These findings provide important insights for the early precise diagnosis and mechanistic exploration of DKD.
Study context
- Population
- Patients with T2D and healthy subjects (urine samples)
- Comparison
- Urine metabolic profiles across diabetic kidney disease (DKD) stages vs healthy subjects
- Outcome
- Urinary metabolite biomarkers of DKD
Methodological detailsliquid chromatography-tandem mass spectrometry, machine learning, multiple reaction monitoring validation
- Liquid chromatography-tandem mass spectrometry
- Machine learning
- Multiple reaction monitoring validation
Performance
As reported in the abstract
Two machine learning algorithms identified five differentially expressed metabolites: hypoxanthine, N-acetyl-DL-histidine, cortisol, tetrahydrobiopterin and L-kynurenine
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Seven biomarkers significantly correlated with clinical indicators (urinary albumin creatinine ratio, serum creatinine) and had early diagnostic value on ROC validation
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Original publication
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