Evidence map›Paper›PMID 30830376›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2018

Diagnosis of Clostridium difficile infection using an UPLC-MS based metabolomics method.

Pengcheng Zhou, Ning Zhou, Li Shao, Jianzhou Li, Sidi Liu, Xiujuan Meng, Juping Duan, Xinrui Xiong, Xun Huang, Yuhua Chen and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Fecal Metabolomics for the Diagnosis ofDiagnostics (Basel, Switzerland) · 2025
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  5. Observational
  6. Advances in diagnostic assays forFrontiers in cellular and infection microbiology · 2024
    Review
  7. Review
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  9. Review
  10. Synergistic effect of co-culture rhizosphereFrontiers in microbiology · 2022
    Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 1 country.

Pengcheng ZhouInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Ning ZhouDepartment of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People's Republic of China.
Li ShaoState 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.
Jianzhou LiDepartment of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shanxi, People's Republic of China.
Sidi LiuInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Xiujuan MengInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Juping DuanInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Xinrui XiongInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Xun HuangInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Yuhua ChenInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Xuegong FanDepartment of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Yixiang ZhengDepartment of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Shujuan MaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, 410078, Hunan, People's Republic of China.
Chunhui LiInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. lichunhui@csu.edu.cn.
Anhua WuInfection Control Center, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, People's Republic of China. xywuanhua@csu.edu.cn.
Central South University · CNXiangya Hospital Central South University · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CNZhejiang University · CN

Funding

National Natural Science Foundation of China 81601803National Science Foundation 31500678Natural Science Foundation of Hunan Province 2017JJ3481Natural Science Foundation of Hunan Province 2017JJ3490Xiangya Sinobio way Health Research Fund xywm2015I11
6 · The paper itself

Abstract

introductionThe 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.

objectiveThe 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.

methodsUltra-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.

resultsDiarrhea 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.

conclusionsCompared 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.

Indexed as

BiomarkersCase-Control StudiesChromatography, High Pressure LiquidClostridioides difficileClostridium InfectionsDiarrheaFecesFemaleHumansMaleMetabolomicsMiddle AgedROC CurveTandem Mass SpectrometryBiomarkersClostridium difficile infectionDiagnostic testMetabolomicsUltra performance liquid chromatography–mass spectrometry

Identifiers

PMID30830376
OpenAlexW2883599356

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.