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

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