Literature DB >> 23819623

Serum metabolic signatures of four types of human arthritis.

Miao Jiang1, Tianlu Chen, Hui Feng, Yinan Zhang, Li Li, Aihua Zhao, Xuyan Niu, Fei Liang, Minzhi Wang, Junping Zhan, Cheng Lu, Xiaojuan He, Lianbo Xiao, Wei Jia, Aiping Lu.   

Abstract

Similar symptoms of the different types of arthritis have continued to confound the clinical diagnosis and represent a clinical dilemma making treatment choices with a more personalized or generalized approach. Here we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects associated with arthritis as well as metabolic signatures of four major types of arthritis--rheumatoid arthritis (n = 27), osteoarthritis (n = 27), ankylosing spondylitis (n = 27), and gout (n = 33)--compared with healthy control subjects (n = 60). A total of 196 metabolites were identified from serum samples using a combined gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-QTOF MS). A global metabolic profile is identified from all arthritic patients, suggesting that there are common metabolic defects resulting from joint inflammation and lesion. Meanwhile, differentially expressed serum metabolites are identified constituting an unique metabolic signature of each type of arthritis that can be used as biomarkers for diagnosis and patient stratification. The results highlight the applicability of metabonomic phenotyping as a novel diagnostic tool for arthritis complementary to existing clinical modalities.

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Year:  2013        PMID: 23819623     DOI: 10.1021/pr400415a

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  29 in total

Review 1.  Biomarker development for axial spondyloarthritis.

Authors:  Matthew A Brown; Zhixiu Li; Kim-Anh Lê Cao
Journal:  Nat Rev Rheumatol       Date:  2020-06-30       Impact factor: 20.543

2.  Serum amino acid metabolic profiles of ankylosing spondylitis by targeted metabolomics analysis.

Authors:  Yong Zhou; Xi Zhang; Rui Chen; Su Han; Yishu Liu; Xuefeng Liu; Ming Gao; Chundong Yang; Daifeng Lu; Beibei Sun; Hao Chen
Journal:  Clin Rheumatol       Date:  2020-03-04       Impact factor: 2.980

Review 3.  Immunometabolism in early and late stages of rheumatoid arthritis.

Authors:  Cornelia M Weyand; Jörg J Goronzy
Journal:  Nat Rev Rheumatol       Date:  2017-03-31       Impact factor: 20.543

4.  Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study.

Authors:  R F Loeser; W Pathmasiri; S J Sumner; S McRitchie; D Beavers; P Saxena; B J Nicklas; J Jordan; A Guermazi; D J Hunter; S P Messier
Journal:  Osteoarthritis Cartilage       Date:  2016-03-21       Impact factor: 6.576

Review 5.  Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis.

Authors:  Xi Xie; Fen Li; Shu Li; Jing Tian; Jin-Wei Chen; Jin-Feng Du; Ni Mao; Jian Chen
Journal:  Clin Rheumatol       Date:  2017-06-10       Impact factor: 2.980

Review 6.  Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity.

Authors:  Erika Dorochow; Michaela Köhm; Lisa Hahnefeld; Robert Gurke
Journal:  J Pers Med       Date:  2022-06-02

7.  Metabolic pathways and immunometabolism in rare kidney diseases.

Authors:  Peter C Grayson; Sean Eddy; Jaclyn N Taroni; Yaíma L Lightfoot; Laura Mariani; Hemang Parikh; Maja T Lindenmeyer; Wenjun Ju; Casey S Greene; Brad Godfrey; Clemens D Cohen; Jeffrey Krischer; Matthias Kretzler; Peter A Merkel
Journal:  Ann Rheum Dis       Date:  2018-05-03       Impact factor: 19.103

Review 8.  Metabolomics in rheumatic diseases: desperately seeking biomarkers.

Authors:  Monica Guma; Stefano Tiziani; Gary S Firestein
Journal:  Nat Rev Rheumatol       Date:  2016-03-03       Impact factor: 20.543

Review 9.  Mixing omics: combining genetics and metabolomics to study rheumatic diseases.

Authors:  Cristina Menni; Jonas Zierer; Ana M Valdes; Tim D Spector
Journal:  Nat Rev Rheumatol       Date:  2017-02-02       Impact factor: 20.543

10.  Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes.

Authors:  Xing Li; Xuezhong Zhou; Yonghong Peng; Baoyan Liu; Runshun Zhang; Jingqing Hu; Jian Yu; Caiyan Jia; Changkai Sun
Journal:  Biomed Res Int       Date:  2014-06-02       Impact factor: 3.411

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