Bei Yan1, Jia Huang2, Chunmei Zhang2, Xin Hu1, Ming Gao2, Aixin Shi1, Weibin Zha3, Luyi Shi1, Cibo Huang2, Liping Yang1. 1. a Department of Clinical Pharmacology & Beijing Key Laboratory of Drug Clinical Risk and Personalized Medication Evaluation , Beijing Hospital , Beijing , P.R. China. 2. b Department of Rheumatology and Immunology , Beijing Hospital , Beijing , P.R. China , and. 3. c Department of Pharmaceutics , University of Washington , Seattle , WA , USA.
Abstract
OBJECTIVES: The aim of this study is to characterize the serum metabolic profiles of patients with systemic lupus erythematosus (SLE) using metabolomics. METHODS: Serum samples were collected from patients with SLE (n = 80) and gender- and age-matched healthy controls (n = 57). Metabolite profiles were performed with gas chromatography-mass spectrometry in conjunction with multivariate statistical analysis, and possible biomarker metabolites were identified. RESULTS: SLE and disease severity-related metabolic phenotypes were identified in sera. Parameters of the metabolomic model were correlated with SLEDAI (SLE disease activity index) scores in SLE. The metabolic signature of SLE patients comprised metabolite changes associated with amino acid turnover or protein biosynthesis, saccharometabolism, lipid metabolism, and gut microbial metabolism. Disease activity-related alterations included glutamate, 2-hydroxyisobutyrate, citrate, glycerol, linoleic acid, and propylparaben metabolites. Parts of endogenous metabolites related to SLE had the relationship with serum immunological parameters and organ manifestations. Moreover, receiver operating characteristic curve analysis revealed a higher diagnosis accuracy of endogenous metabolites. CONCLUSIONS: Our study distinguished serum metabotypes associated with SLE and disease activities. The implementation of this metabolomic strategy may help to develop biochemical insight into the metabolic alterations in SLE.
OBJECTIVES: The aim of this study is to characterize the serum metabolic profiles of patients with systemic lupus erythematosus (SLE) using metabolomics. METHODS: Serum samples were collected from patients with SLE (n = 80) and gender- and age-matched healthy controls (n = 57). Metabolite profiles were performed with gas chromatography-mass spectrometry in conjunction with multivariate statistical analysis, and possible biomarker metabolites were identified. RESULTS:SLE and disease severity-related metabolic phenotypes were identified in sera. Parameters of the metabolomic model were correlated with SLEDAI (SLE disease activity index) scores in SLE. The metabolic signature of SLEpatients comprised metabolite changes associated with amino acid turnover or protein biosynthesis, saccharometabolism, lipid metabolism, and gut microbial metabolism. Disease activity-related alterations included glutamate, 2-hydroxyisobutyrate, citrate, glycerol, linoleic acid, and propylparaben metabolites. Parts of endogenous metabolites related to SLE had the relationship with serum immunological parameters and organ manifestations. Moreover, receiver operating characteristic curve analysis revealed a higher diagnosis accuracy of endogenous metabolites. CONCLUSIONS: Our study distinguished serum metabotypes associated with SLE and disease activities. The implementation of this metabolomic strategy may help to develop biochemical insight into the metabolic alterations in SLE.