Metabolic Signature Profiling as a Diagnostic and Prognostic Tool in Pediatric Plasmodium falciparum Malaria
Izabella Surowiec1, Judy Orikiiriza2, Elisabeth Karlsson3
1Computational Life Science Cluster, Department of Chemistry.
Insights
This study introduces a metabolomics approach for diagnosing and staging malaria in children. Metabolite profiling accurately distinguishes malaria severity and identifies subgroups within mild cases, aiding prognosis.
Area of Science:
- Medical research
- Biochemistry
- Infectious diseases
Background:
- Accurate malaria diagnosis and staging are critical for reducing mortality and long-term health issues.
- Current diagnostic methods may require enhancement for precise staging.
- Plasmodium falciparum infection poses a significant global health burden, particularly in children.
Purpose of the Study:
- To develop and evaluate a metabolomics approach for the diagnostic staging of malaria infections caused by Plasmodium falciparum in children.
- To assess the potential of metabolite profiling for differentiating between mild and severe malaria.
- To explore the utility of metabolomics in identifying prognostic subgroups within malaria patients.
Main Methods:
- A cohort of 421 children (6 months to 6 years) with mild malaria, severe malaria, and age-matched controls were analyzed.
- Plasma samples from a representative selection of patients (20 per category) underwent gas chromatography-mass spectrometry (GC-MS) for comprehensive metabolite profiling.
- A proof-of-concept study utilized a Plasmodium berghei in vivo model to assess metabolic profile changes over infection time.
Main Results:
- Principal component analysis of metabolite profiles effectively separated patients into distinct disease categories (mild malaria, severe malaria, controls), independent of clinical data.
- Two distinct subgroups were identified within the mild malaria cohort, suggesting different prognoses.
- Metabolic profiles in the Plasmodium berghei model demonstrated discernibility over the course of infection.
Conclusions:
- Metabolite signature profiling shows promise as a decision support tool for malaria diagnosis and disease staging.
- This approach can aid in prognostication by identifying patient subgroups with potentially divergent outcomes.
- Metabolomics offers a novel, data-driven strategy to enhance malaria management.
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.
