Acta Academiae Medicinae Sinica

Acta Academiae Medicinae Sinica

Acta Academiae Medicinae Sinica ›› 2011, Vol. 33 ›› Issue (5): 511-516.doi: 10.3881/j.issn.1000-503X.2011.05.007

• Original Articles • Previous Articles     Next Articles

Analyzing Urinary Proteome Patterns of Metabolic Syndrome Patients with Early Renal Injury by Magnet Bead Separation and Matrix assisted Laser Desorption Ionization Time of flight Mass Spectrometry

GAO Bi-xia1, 2, LI Ming-xi1, 2, LIU Xue-jiao1, 2, CAI Jian-fang1, 2   

  1. 1Department of Nephrology, 2Translational Medicine Center, PUMC Hospital, CAMS and PUMC, Beijing 100730, China3Department of Biomedical Engineering, Institute of Basic Medical Sciences, CAMS and PUMC, Beijing 100005, China
  • Received:2011-04-06 Revised:2011-10-28 Online:2011-10-28 Published:2011-10-28
  • Contact: LI Ming-xi E-mail:mingxili@hotmail.com
  • Supported by:

    Supported by the Ministry of Health's Special Fund for Public Welfare(200802007)

Abstract: Objective To determine the potential urinary biomarkers of metabolic syndrome (MS) with early renal injury and establish diagnostic models by magnetic bead-based separation and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Methods Participants were selected from the epidemiologic study on MS and renal involvement among residents in Pinggu district, Beijing. Eight-hour overnight urine samples were fractionated by means of magnetic bead-based weak cation exchange chromatography and subsequently analyzed with MALDI-TOF MS. Wilcoxon test and random forests were used to screen differential protein peaks of MS patients with early renal injury, then combined with genetic algorithm and support vector machine, respectively, to establish diagnostic models. Results Totally 54 cases of MS without renal injury and 46 cases of MS with early renal injury were enrolled. Totally twenty protein peaks were up-regulated in the urine of MS patients with early renal injury by Wilcoxon test (P0.005). Genetic algorithm based model showed 82.6% sensitivity, 84.3% specificity, and 83.5% accuracy by a 10-fold cross-validation in identifying MS patients with early renal injury; correspondingly, the support vector machine based model reported 89.2% sensitivity, 81.1% specificity and 85.5 % accuracy. Four protein peaks were included in two diagnostic models with mass-to-charge ratios of 2756.98, 3019.11, 9077.04, and 10054.26. Conclusions The urinary proteome patterns of MS with early renal injury were successfully established with magnetic bead-based separation and MALDI-TOF MS technology. A series of urinary differential expressing protein peaks were identified with bioinformatics tools. Diagnostic models combining cluster of protein peaks are capable of differentiating MS patients with early renal injury from those without renal injury. The different urine protein excretion patterns revealed in this study provide urinary candidate biomarkers of MS patients with early renal injury for future identification and biological roles investigation.

Key words: metabolic syndrome, renal injury, urinary biomarkers, magnetic bead, matrix-assisted laser desorption ionization time-of-flight mass spectrometry

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