Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy
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Capillary electrophoresis coupled with time-of-flight mass spectrometry was used to explore new serum biomarkers with high sensitivity and specificity for diabetic nephropathy (DN) diagnosis, through comprehensive analysis of serum metabolites with 78 diabetic patients. Multivariate analyses were used for identification of marker candidates and development of discriminative models. Of the 289 profiled metabolites, orthogonal partial least-squares discriminant analysis identified 19 metabolites that could distinguish between DN with macroalbuminuria and diabetic patients without albuminuria. These identified metabolites included creatinine, aspartic acid, γ-butyrobetaine, citrulline, symmetric dimethylarginine (SDMA), kynurenine, azelaic acid, and galactaric acid. Significant correlations between all these metabolites and urinary albumin-to-creatinine ratios (p < 0.009, Spearman’s rank test) were observed. When five metabolites (including γ-butyrobetaine, SDMA, azelaic acid and two unknowns) were selected from 19 metabolites and applied for multiple logistic regression model, AUC value for diagnosing DN was 0.927 using the whole dataset, and 0.880 in a cross-validation test. In addition, when four known metabolites (aspartic acid, SDMA, azelaic acid and galactaric acid) were applied, the resulting AUC was still high at 0.844 with the whole dataset and 0.792 with cross-validation. Combination of serum metabolomics with multivariate analyses enabled accurate discrimination of DN patients. The results suggest that capillary electrophoresis-mass spectrometry based metabolome analysis could be used for DN diagnosis.
KeywordsDiabetic nephropathy Capillary electrophoresis-mass spectrometry Metabolome Biomarker Multiple logistic regression Orthogonal partial least-squares discriminant analysis
We thank Dr. Astuko Watarai, Japan Labour Health and Welfare Organization Chubu Rosai Hospital, for assistance with sample collection. We also thank Maki Sugawara and Hiroko Ueda, Institute for Advanced Biosciences, Keio University, and Jiro Nakamura, Department of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, for technical support and fruitful discussions. This work was supported by a Health and Labour Sciences Research Grant “Research on Biological Markers for New Drug Development”, Grants from the Ministry of Health, Labour and Welfare of Japan “Research on Rare and Intractable Disease”, KAKENHI Grants-in-Aid for Scientific Research on Priority Areas “Systems Genomes” and “Lifesurveyor” from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and research funds from the Yamagata prefectural government and the City of Tsuruoka.
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