Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy

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 us...

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Bibliographic Details
Published in:Analytical and bioanalytical chemistry 2012-12, Vol.404 (10), p.3101-3109
Main Authors: Hirayama, Akiyoshi, Nakashima, Eitaro, Sugimoto, Masahiro, Akiyama, Shin-ichi, Sato, Waichi, Maruyama, Shoichi, Matsuo, Seiichi, Tomita, Masaru, Yuzawa, Yukio, Soga, Tomoyoshi
Format: Article
Language:English
Subjects:
Aged
Analysis
Analytical Chemistry
Aspartate
Aspartic acid
Biochemistry
Biological markers
Biomarkers
Biomarkers - blood
Biomarkers - metabolism
Capillarity
Care and treatment
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Diabetic nephropathies
Diabetic Nephropathies - blood
Diabetic Nephropathies - metabolism
Diabetic Nephropathies - pathology
Diagnosis
Discriminant Analysis
Electrophoresis, Capillary - methods
Female
Food Science
Health aspects
Humans
Laboratory Medicine
Male
Mass Spectrometry - methods
Metabolites
Metabolome
Metabolomics - methods
Middle Aged
Monitoring/Environmental Analysis
Multivariate Analysis
Original Paper
Patients
Risk factors
ROC Curve
Serums
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Summary: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  
ISSN:1618-2642
1618-2650
DOI:10.1007/s00216-012-6412-x