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Diagnosis of Clostridium difficile infection using an UPLC-MS based metabolomics method.
Zhou, Pengcheng; Zhou, Ning; Shao, Li; Li, Jianzhou; Liu, Sidi; Meng, Xiujuan; Duan, Juping; Xiong, Xinrui; Huang, Xun; Chen, Yuhua; Fan, Xuegong; Zheng, Yixiang; Ma, Shujuan; Li, Chunhui; Wu, Anhua.
Afiliación
  • Zhou P; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Zhou N; Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
  • Shao L; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China.
  • Li J; Department of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shanxi, People's Republic of China.
  • Liu S; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Meng X; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Duan J; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Xiong X; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Huang X; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Chen Y; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Fan X; Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
  • Zheng Y; Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
  • Ma S; Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan, People's Republic of China.
  • Li C; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. lichunhui@csu.edu.cn.
  • Wu A; Infection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. xywuanhua@csu.edu.cn.
Metabolomics ; 14(8): 102, 2018 07 19.
Article en En | MEDLINE | ID: mdl-30830376
INTRODUCTION: The fecal metabolome of Clostridium difficile (CD) infection is far from being understood, particularly its non-volatile organic compounds. The drawbacks of current tests used to diagnose CD infection hinder their application. OBJECTIVE: The aims of this study were to find new characteristic fecal metabolites of CD infection and develop a metabolomics model for the diagnosis of CD infection. METHODS: Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was used to characterize the fecal metabolome of CD positive and negative diarrhea and healthy control stool samples. RESULTS: Diarrhea and healthy control samples showed distinct clusters in the principal components analysis score plot, and CD positive group and CD negative group demonstrated clearer separation in a partial least squares discriminate analysis model. The relative abundance of sphingosine, chenodeoxycholic acid, phenylalanine, lysophosphatidylcholine (C16:0), and propylene glycol stearate was higher, and the relative abundance of fatty amide, glycochenodeoxycholic acid, tyrosine, linoleyl carnitine, and sphingomyelin was lower in CD positive diarrhea groups, than in the CD negative group. A linear discriminant analysis model based on capsiamide, dihydrosphingosine, and glycochenodeoxycholic acid was further constructed to identify CD infection in diarrhea. The leave-one-out cross-validation accuracy and area under receiver operating characteristic curve for the training set/external validation set were 90.00/78.57%, and 0.900/0.7917 respectively. CONCLUSIONS: Compared with other hospital-onset diarrhea, CD diarrhea has distinct fecal metabolome characteristics. Our UPLC-MS metabolomics model might be useful tool for diagnosing CD diarrhea.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clostridioides difficile / Cromatografía Líquida de Alta Presión / Infecciones por Clostridium / Diarrea / Espectrometría de Masas en Tándem / Metabolómica / Heces Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Metabolomics Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clostridioides difficile / Cromatografía Líquida de Alta Presión / Infecciones por Clostridium / Diarrea / Espectrometría de Masas en Tándem / Metabolómica / Heces Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Metabolomics Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos