title: Metabolic Signature Profiling as a Diagnostic and Prognostic Tool in Pediatric Plasmodium falciparum Malaria.
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.
authors.affiliation: Computational Life Science Cluster, Department of Chemistry.
authors.forename: Izabella
authors.initials: I
authors.lastname: Surowiec
authors.affiliation: Infectious Diseases Institute, School of Medicine and Health Sciences , Makerere University , Uganda ; Department of Immunology , Trinity College , Dublin , Ireland.
authors.forename: Judy
authors.initials: J
authors.lastname: Orikiiriza
authors.affiliation: Department of Molecular Biology.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Karlsson
authors.affiliation: Department of Molecular Biology.
authors.forename: Maria
authors.initials: M
authors.lastname: Nelson
authors.affiliation: Department of Molecular Biology.
authors.forename: Mari
authors.initials: M
authors.lastname: Bonde
authors.affiliation: School of Medicine and Pharmacy, College of Medicine and Health Sciences , University of Rwanda , Butare.
authors.forename: Patrick
authors.initials: P
authors.lastname: Kyamanwa
authors.affiliation: Rwanda Military Hospital , Kigali.
authors.forename: Ben
authors.initials: B
authors.lastname: Karenzi
authors.affiliation: Department of Molecular Biology ; Laboratory for Molecular Infection Medicine Sweden, Umeå University ; Umeå Center for Microbial Research , Sweden.
authors.forename: Sven
authors.initials: S
authors.lastname: Bergström
authors.affiliation: Computational Life Science Cluster, Department of Chemistry.
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
authors.affiliation: Division 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.
authors.forename: Johan
authors.initials: J
authors.lastname: Normark
keywords: disease staging; malaria; metabolomics