Metabolic Signature Profiling as a Diagnostic and Prognostic Tool in Pediatric Plasmodium falciparum Malaria. (4th May 2015)

• Record Type: Journal Article
• Authors: Surowiec, Izabella; Orikiiriza, Judy; Karlsson, Elisabeth; Nelson, Maria; Bonde, Mari; Kyamanwa, Patrick; Karenzi, Ben; Bergström, Sven; Trygg, Johan; Normark, Johan
• Is Part Of: Open forum infectious diseases. Volume 2:Number 2(2015)
• Publication Date: 2015-05-04
• Subjects: disease staging -- malaria -- metabolomics; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofv062
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract : The metabolic profile in paediatric patients suffering from acute P. falciparum malaria carries sufficient information to grade disease severity. 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 ide.jpegied 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.