Clinical Study
Application of metabolomics in establishing primary nephrotic syndrome diagnosis model
Xiaobo Zhang, Ju Li, Shanlei Qiao, Yankai Xia, Fengying Tang
Published 2016-05-15
Cite as Chin J Nephrol, 2016, 32(5): 334-338. DOI: 10.3760/cma.j.issn.1001-7097.2016.05.003
Abstract
ObjectiveTo establish diagnosis model and explore related metabolic pathways by analyzing the serum metabolic profile of patients with primary nephrotic syndrome (PNS) through metabolomics.
MethodsThirty PNS patients hospitalized in Huai'an First People's Hospital between December 2010 and April 2012 were enrolled. High performance liquid chromatography-mass spectrometry (LC-MS) was employed to detect metabolites in the serum of 30 PNS patients and 30 healthy controls. Metabolic fingerprint profiling and multivariate pattern recognition analysis were combined to establish disease-specific metabolic diagnosis model, and metabolic pathway analysis was performed.
ResultsPNS group and control group could be well separated by principal component analysis (PCA) model as well as partial least-squares discriminant analysis (PLS-DA) model with Q2 of 0.300. There was well interpretation in PLA-DA model (R2X=0.581, R2Y=0.452). Compared with healthy controls, PNS patients had decreased cholestane 3, 7, 12, 15 alcohol, acyl glycerine, phytosphingosine and tryptophan, and increased sphingomyelin, arginine and glutamic acid (all VIP>1, P<0.05). The metabolic disorders pathways of PNS patients included sphingolipid metabolism, arginine and proline metabolism, linoleic acid metabolism and pyrimidine metabolism (all impact> 0.10 and P<0.05).
ConclusionsMetabolomics combined with multivariate pattern recognition analysis may be a new tool for diagnosis and monitoring of PNS.
Key words:
Nephrotic syndrome; Diagnostic techniques and procedures; Metabolomics
Contributor Information
Xiaobo Zhang
Department of Nephrology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an 223300, China
Ju Li
Shanlei Qiao
Yankai Xia
Fengying Tang