Metabolic signature profiling as a diagnostic and prognostic tool in paediatric Plasmodium falciparum malaria

Surowiec, Izabella (author): Umeå universitet,Kemiska institutionen,Computational Life Science Cluster

Orikiiriza, Judy (author)

Karlsson, Elisabeth (author): Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten)

Nelson, Maria (author): Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten)

Bonde, Mari (author): Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten)

Kyamanwa, Patrick (author)

Karenzi, Ben (author)

Bergström, Sven (author): Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten),Umeå Centre for Microbial Research (UCMR),Molekylär Infektionsmedicin, Sverige (MIMS)

Trygg, Johan (author): Umeå universitet,Kemiska institutionen,Computational Life Science Cluster

Normark, Johan (author): Umeå universitet,Molekylär Infektionsmedicin, Sverige (MIMS),Umeå Centre for Microbial Research (UCMR),Infektionssjukdomar,nfectious Diseases Institute, School of Medicine and Health Sciences, Makerere University, Uganda

(creator_code:org_t)

2015-05-04
2015
English.
In: Open Forum Infectious Diseases. - : Oxford University Press. - 2328-8957. ; 2:2

Related links:: https://doi.org/10.1...; show more...; https://umu.diva-por... (primary) (Raw object); https://academic.oup...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

Background: Accuracy in malaria diagnosis and staging is vital in order to reduce mortality and post infectious sequelae. Herein we present a metabolomics approach to diagnostic staging of malaria infection, specifically Plasmodium falciparum infection in children. Methods: A group of 421 patients between six months and six 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 twenty 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 two-component principal component analysis (PCA) revealed that the patients could be separated into disease categories according to metabolite profiles, independently of any clinical information. Furthermore, two sub-groups could be identified in the mild malaria cohort who we believe represent patients with divergent prognoses. Conclusion: Metabolite signature profiling could be used both for decision support in disease staging and prognostication.

By the author/editor: Surowiec, Izabel ...; Orikiiriza, Judy; Karlsson, Elisab ...; Nelson, Maria; Bonde, Mari; Kyamanwa, Patric ...; show more...; Karenzi, Ben; Bergström, Sven; Trygg, Johan; Normark, Johan; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Infectious Medic ...

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