Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Nov 24;8(69):114239-114250.
doi: 10.18632/oncotarget.23207. eCollection 2017 Dec 26.

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

Lin Sun  1 , Biao Xie  1 , Qiuju Zhang  1 , Yupeng Wang  1 , Xinyu Wang  1 , Bing Gao  1 , Meina Liu  1 , Maoqing Wang  2
Affiliations

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

Lin Sun et al. Oncotarget. .

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.

Keywords: HSP; HSPN; biomarkers; clinical; metabonomics.

PubMed Disclaimer

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
(A) Age and gender distribution of HSP children; (B) predisposing factors for HSP; (C) seasonal distribution of HSP onset.
Figure 2
Figure 2
(A) PLS-DA score plot for HSP vs. HC; (B) PLS-DA score plot for HSPWN vs. HC; (C) PLS-DA score plot for HSPN vs. HC; (D) PLS-DA score plot for HSPN vs. HSPWN; (E) validation plot for HSP vs. HC; (F) validation plot for HSPWN vs. HC; (G) validation plot for HSPN vs. HC; (H) validation plot for HSP vs. HSPWN.
Figure 3
Figure 3. Venn plot for all biomarkers in two-two comparison among three groups
Figure 4
Figure 4
Changing patterns of differential metabolites from HC group across HSPWN and HSPN groups: (A) Type A biomarkers; (B) Type B biomarkers; (C) Type C biomarkers.
Figure 5
Figure 5
(A) ROC curves of biomarkers for HSP prediction; (B) ROC curves of biomarkers for HSPN prediction.
Figure 6
Figure 6. An overview of workflow utilized in serum metabonomics analysis of HSP
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Saulsbury FT. Henoch-Schönlein purpura. Curr Opin Rheumatol. 2010;22:598–602. - PubMed
    1. Trnka P. Henoch-Schönlein purpura in children. J Paediatr Child Health. 2013;49:995–1003. - PubMed
    1. Yang YH, Yu HH, Chiang BL. The diagnosis and classification of Henoch-Schönlein purpura: an updated review. Autoimmun Rev. 2014;13:355–358. - PubMed
    1. Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature. 2008;455:1054–1056. - PubMed
    1. Ozen S, Ruperto N, Dillon MJ, Bagga A, Barron K, Davin JC, Kawasaki T, Lindsley C, Petty RE, Prieur AM, Ravelli A, Woo P. EULAR/PReS endorsed consensus criteria for the classification of childhood vasculitides. Ann Rheum Dis. 2006;65:936–941. - PMC - PubMed

LinkOut - more resources