Malaria metabolomic signatures across human hosts, Plasmodium parasites, and Anopheles vectors: a systematic evidence synthesis

Malaria continues to be a significant health challenge worldwide, causing 282 million cases and 610,000 deaths in 2024, especially in sub-Saharan Africa. The use of genomic and transcriptomic tools has improved understanding of host, parasite, and vector biology; however, these tools have not captured the functional and phenotypic impacts of infection and resistance. A systematic review (SR) was carried out in accordance with the PRISMA 2020 guidelines in the PubMed, Scopus, and Web of Science databases using the combination of terms “metabolomics” and “malaria/Plasmodium/Anopheles”. 885 records from 3 databases were identified; 352 were discarded as duplicates, leaving 533 for screening. Of these, 70 articles underwent full-text review, and 51 were included in the final screening. Because of the biological diversity, the data were stratified by target organism (host, parasite or vector) and were then synthesised narratively (SWiM guidelines). In the vector, insecticide resistance and Plasmodium infection were associated with altered metabolism of glycerophospholipids, amino acids, and purines, including swaps between tryptophan-serotonin and lipid reproduction that might affect transmission competence. Within the human host, the acidosis (lactate, hydroxybutyrate), arginine and aromatic amino acid deficiency, and dysregulated lipid/steroid metabolism were frequently reported to be associated with disease severity. Pressures exerted by anti-malarial drugs resulted in conserved disruption of haemoglobin pyrimidine metabolism in the parasite under fast-acting drugs. In contrast, under sub-lethal or resistance-associated conditions, drug-specific rerouting of lipid, carbohydrate and nucleotide pathways was observed. The most commonly used analytical platforms in this study were liquid chromatography-mass spectrometry (LC-MS/MS), followed by other methods. The results are mainly descriptive, with no causal validation, standardised platforms, and field-representative sampling. For future applications, integration of multi-omics data, harmonisation of reporting and longitudinal and cross-population validation are priorities to transform these metabolic signatures into clinical and vector-control applications. Not applicable.