Oncotarget

Research Papers:

Biomarkers identification by a combined clinical and metabonomics analysis in Henoch-Schonlein purpura nephritis children

Lin Sun, Biao Xie, Qiuju Zhang, Yupeng Wang, Xinyu Wang, Bing Gao, Meina Liu _ and Maoqing Wang

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Oncotarget. 2017; 8:114239-114250. https://doi.org/10.18632/oncotarget.23207

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Abstract

Lin Sun1, Biao Xie1, Qiuju Zhang1, Yupeng Wang1, Xinyu Wang1, Bing Gao1, Meina Liu1 and Maoqing Wang2

1Department of Epidemiology and Biostatistics, Public Health College, Harbin Medical University, Harbin, P. R. China

2Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China

Correspondence to:

Meina Liu, email: liumeina369@163.com

Maoqing Wang, email: wang_maoqing@126.com

Keywords: HSP; HSPN; biomarkers; metabonomics; clinical

Received: September 08, 2017    Accepted: October 29, 2017    Published: November 24, 2017

ABSTRACT

Background: In children with Henoch-Schonlein purpura (HSP), the severity of Henoch-Schonlein purpura nephritis (HSPN) is considered responsible for the prognosis of HSP. The pathological process from HSP to HSPN is not clear yet and current diagnostic tools have shortcomings in accurate diagnosis of HSPN. This study aims to assess clinical characteristics of HSP and HSPN, to identify metabolic perturbations involved in HSP progress, and to combine metabolic biomarkers and clinical features into a better prediction for HSPN.

Methods: A total of 162 children were recruited, including 109 HSP patients and 53 healthy children (HC). The clinical characteristics were compared between HSPN and HSP without nephritis (HSPWN). The serum metabonomics analysis was performed to determine the metabolic differences in HSP and HC.

Results: Among 109 HSP children, 57 progressed to HSPN. The increased D-dimer level was significantly associated with renal damage in HSP. The metabonomic profiles revealed alterations between various subgroups of HSP and HC, making it possible to investigate small-molecule metabolites related to the pathological process of HSP. In total, we identified 9 biomarkers for HSP vs. HC, 7 for HSPWN vs. HC, 9 for HSPN vs. HC, and 3 for HSPN vs. HSPWN.

Conclusions: (S)-3-hydroxyisobutyric acid, p-Cresol sulfate, and 3-carboxy-4-methyl-5-pentyl-2-furanpropanoic acid were found associated with the progress of HSP to HSPN. Moreover, resulting biomarkers, when combined with D-dimer, allowed improving the HSPN prediction with high sensitivity (94.7%) and specificity (80.8%). Together these findings highlighted the strength of the combination of metabonomics and clinical analysis in the research of HSP.


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