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Updated: Mar 22, 2026
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Early prediction of cerebral malaria by (1)H NMR based metabolomics
Soumita Ghosh1, Arjun Sengupta1, Shobhona Sharma2
1Department of Chemical Sciences, Tata Institute of Fundamental Research, 1-Homi Bhabha Road, Mumbai, 400 005, India.
Background:
Cerebral malaria (CM) is a life-threatening disease, caused mainly by Plasmodium falciparum in humans. In adults only 1-2% of P. falciparum-infected hosts transit to the cerebral form of the disease while most exhibit non-cerebral malaria (NCM). The perturbed metabolic pathways of CM and NCM have been reported. Early marker(s) of CM is(are) not known and by the time a patient exhibits the pathological symptoms of CM, the disease has progressed. Murine CM, like the human disease, is difficult to assign to specific animals at early stage and hence the challenge to treat CM at pre-clinical stage of the disease. This is the first report of prediction of CM in mice using a novel strategy based on (1)H nuclear magnetic resonance (NMR)-based metabolomics.
Methods:
Mice were infected with malarial parasites, and serum was collected from all the animals (CM/NCM) before CM symptoms were apparent. The assignment of mice as NCM/CM at an early time point is based on their symptoms at days 8-9 post-infection (pi). The serum samples were subjected to (1)H NMR-based metabolomics. (1)H NMR spectra of the serum samples, collected at various time points (pi) in multiple sets of experiments, were subjected to multivariate analyses.
Results:
The results from orthogonal partial least square discriminant analyses (OPLS-DA) suggest that the animals with CM start to diverge out in metabolic profile and were distinct on day 4 pi, although by physical observation they were indistinguishable from the NCM. The metabolites that appeared to contribute to this distinction were serum lipids and lipoproteins, and 14-19% enhancement was observed in mice afflicted with CM. A cut-off of 14% change of total lipoproteins in serum predicts 54-71% CM in different experiments at day 4 pi.
Conclusion:
This study clearly demonstrates the possibility of differentiating and identifying animals with CM at an early, pre-clinical stage. The strategy, based on metabolite profile of serum, tested with different batches of animals in both the sex and across different times of the year, is found to be robust. This is the first such study of pre-clinical prognosis of CM.
Insights
This study predicts cerebral malaria (CM) in mice using NMR metabolomics. Early detection of CM is possible by analyzing serum lipid profiles before symptoms appear.
Area of Science:
- Biochemistry
- Medical Science
- Parasitology
Background:
- Cerebral malaria (CM) is a severe complication of Plasmodium falciparum infection, with limited early diagnostic markers.
- Distinguishing between CM and non-cerebral malaria (NCM) in early stages, particularly in preclinical models, remains challenging.
- Current diagnostic methods for CM often identify the disease only after significant pathological progression.
Purpose of the Study:
- To develop a novel strategy for the early prediction of CM in a murine model.
- To identify reliable early biomarkers for CM using metabolomics.
- To enable pre-clinical intervention by detecting CM before overt symptoms manifest.
Main Methods:
- Serum samples were collected from mice infected with malarial parasites at various time points post-infection.
- Proton nuclear magnetic resonance ((1)H NMR)-based metabolomics was employed to analyze serum samples.
- Multivariate statistical analyses, including orthogonal partial least square discriminant analysis (OPLS-DA), were applied to metabolic profiles.
Main Results:
- Metabolic profiles of mice with CM began to diverge from NCM as early as day 4 post-infection, prior to observable symptoms.
- Serum lipids and lipoproteins were identified as key distinguishing metabolites, showing a 14-19% enhancement in CM-afflicted mice.
- A 14% change in total serum lipoproteins effectively predicted CM with 54-71% accuracy in different experimental settings.
Conclusions:
- The study successfully demonstrated the feasibility of differentiating and identifying CM in its early, pre-clinical stage.
- The developed NMR-based metabolomics strategy proved robust across different animal batches, sexes, and seasons.
- This research represents the first report of a pre-clinical prognosis for CM using metabolomic profiling.
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