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The Scientific World Journal
Volume 2013 (2013), Article ID 268407, 10 pages
Research Article

Serum N-Glycan Profiling Predicts Prognosis in Patients Undergoing Hemodialysis

1Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
2Faculty of Advanced Life Science and Frontier Research Center for Post-Genome Science and Technology, Hokkaido University, Sapporo 001-0021, Japan
3Department of Advanced Transplant and Regenerative Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
4Department of Radiological Technology, Hirosaki University School of Health Sciences, Hirosaki 036-8562, Japan
5Department of Urology, Oyokyo Kidney Research Institute, Hirosaki 036-8243, Japan

Received 14 October 2013; Accepted 26 November 2013

Academic Editors: A. R. Nissenson and H. Yamabe

Copyright © 2013 Shingo Hatakeyama et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. The aim of this study is to evaluate the usefulness of serum N-glycan profiling for prognosis in hemodialysis patients. Methods. Serum N-glycan analysis was performed in 100 hemodialysis patients in June 2008 using the glycoblotting method, which allows high-throughput, comprehensive, and quantitative N-glycan analysis. All patients were longitudinally followed up for 5 years. To evaluate the independent predictors for prognosis, patients' background, blood biochemistry, and N-glycans intensity were analyzed using Cox regression multivariate analysis. Selected N-glycans and independent factors were evaluated using the log-rank test with the Kaplan-Meier method to identify the predictive indicators for prognosis. Each patient was categorized according to the number of risk factors to evaluate the predictive potential of the risk criteria for prognosis. Results. In total, 56 N-glycan types were identified in the hemodialysis patients. Cox regression multivariate analysis showed cardiovascular events, body mass index, maximum intima media thickness, and the serum N-glycan intensity of peak number 49 were predictive indicators for overall survival. Risk classification according to the number of independent risk factors revealed significantly poor survival by increasing the number of risk factors. Conclusions. Serum N-glycan profiling may have a potential to predict prognosis in patients undergoing hemodialysis.