Evidence map›Paper›PMID 26110164›Full record

ArticleOpen forum infectious diseases2015

Metabolic Signature Profiling as a Diagnostic and Prognostic Tool in Pediatric Plasmodium falciparum Malaria.

Izabella Surowiec, Judy Orikiiriza, Elisabeth Karlsson, Maria Nelson, Mari Bonde, Patrick Kyamanwa, Ben Karenzi, Sven Bergström, Johan Trygg, Johan Normark

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
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

19 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Biologically ActiveFrontiers in pharmacology · 2019
    Article
  12. Article
  13. Review
  14. Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria.International journal of medical microbiology : IJMM · 2017
    Article
  15. Article
  16. Article
  17. Lipid response patterns in acute phase paediatricMetabolomics : Official journal of the Metabolomic Society · 2017
    Article
  18. Article
  19. 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

10 authors.

Izabella SurowiecComputational Life Science Cluster, Department of Chemistry.
Judy OrikiirizaInfectious Diseases Institute, School of Medicine and Health Sciences , Makerere University , Uganda ; Department of Immunology , Trinity College , Dublin , Ireland.
Elisabeth KarlssonDepartment of Molecular Biology.
Maria NelsonDepartment of Molecular Biology.
Mari BondeDepartment of Molecular Biology.
Patrick KyamanwaSchool of Medicine and Pharmacy, College of Medicine and Health Sciences , University of Rwanda , Butare.
Ben KarenziRwanda Military Hospital , Kigali.
Sven BergströmDepartment of Molecular Biology ; Laboratory for Molecular Infection Medicine Sweden, Umeå University ; Umeå Center for Microbial Research , Sweden.
Johan TryggComputational Life Science Cluster, Department of Chemistry.
Johan NormarkDivision of Infectious Diseases, Department Clinical Microbiology , Umeå University , Sweden ; Infectious Diseases Institute, School of Medicine and Health Sciences , Makerere University , Uganda ; Laboratory for Molecular Infection Medicine Sweden, Umeå University ; Umeå Center for Microbial Research , Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background.  Accuracy in malaria diagnosis and staging is vital to reduce mortality and post infectious sequelae. In this study, we present a metabolomics approach to diagnostic staging of malaria infection, specifically Plasmodium falciparum infection in children. Methods.  A group of 421 patients between 6 months and 6 years of age with mild and severe states of malaria with age-matched controls were included in the study, 107, 192, and 122, individuals, respectively. A multivariate design was used as basis for representative selection of 20 patients in each category. Patient plasma was subjected to gas chromatography-mass spectrometry analysis, and a full metabolite profile was produced from each patient. In addition, a proof-of-concept model was tested in a Plasmodium berghei in vivo model where metabolic profiles were discernible over time of infection. Results.  A 2-component principal component analysis revealed that the patients could be separated into disease categories according to metabolite profiles, independently of any clinical information. Furthermore, 2 subgroups could be identified in the mild malaria cohort who we believe represent patients with divergent prognoses. Conclusions.  Metabolite signature profiling could be used both for decision support in disease staging and prognostication.

Indexed as

disease stagingmalariametabolomics

Identifiers

PMID26110164
PMCPMC4473097

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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.