Evidence map›Paper›PMID 26927094›Full record

ArticleInternational journal of molecular sciences2016

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis.

Susanna K P Lau, Kim-Chung Lee, George C S Lo, Vanessa S Y Ding, Wang-Ngai Chow, Tony Y H Ke, Shirly O T Curreem, Kelvin K W To, Deborah T Y Ho, Siddharth Sridhar and 7 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

14 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
  2. Article
  3. Plasma Metabolomics Reveals Distinct Biological and Diagnostic Signatures for Melioidosis.American journal of respiratory and critical care medicine · 2024
    Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Clinical Mass Spectrometry in the Bioinformatics Era: A Hitchhiker's Guide.Computational and structural biotechnology journal · 2018
    Review
  12. Article
  13. Article
  14. 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

17 authors at 2 institutions in 1 country.

Susanna K P LauState Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China. skplau@hkucc.hku.hk.
Kim-Chung LeeDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. leekimchung1983@gmail.com.
George C S LoDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. gcslo@connect.hku.hk.
Vanessa S Y DingDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. sydin1@hku.hk.
Wang-Ngai ChowDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. cwn5810@gmail.com.
Tony Y H KeDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. kyh416@hotmail.com.
Shirly O T CurreemDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. shirly.curreem@gmail.com.
Kelvin K W ToState Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China. kelvinto@hkucc.hku.hk.
Deborah T Y HoDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. tipyin@yahoo.com.hk.
Siddharth SridharDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. sid8998@gmail.com.
Sally C Y WongDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. wcy288@ha.org.hk.
Jasper F W ChanDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. jfwchan@hku.hk.
Ivan F N HungResearch Centre of Infection and Immunology, The University of Hong Kong, Pokfulam, Hong Kong, China. ivanhung@hku.hk.
Kong-Hung SzeDepartment of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong, China. khsze@hku.hk.
Ching-Wan LamDepartment of Pathology, The University of Hong Kong, Pokfulam, Hong Kong, China. ching-wanlam@pathology.hku.hk.
Kwok-Yung YuenState Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China. kyyuen@hkucc.hku.hk.
Patrick C Y WooState Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China. pcywoo@hkucc.hku.hk.
Chinese University of Hong Kong · HKUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.

Indexed as

MetabolomeBacteremiaBiomarkersCarnitineCase-Control StudiesHumansMelioidosisPhosphatidylcholinesSphingomyelinsacylcarnitineBiomarkersCarnitinePhosphatidylcholinesSphingomyelinsbiomarkersBurkholderia pseudomalleimelioidosismetabolomicsplasma

Identifiers

PMID26927094
PMCPMC4813170
OpenAlexW2283132095

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.