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Reviews

The spectrum of clinical biomarkers in severe malaria and new avenues for exploration

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 634-653 | Received 10 Dec 2021, Accepted 18 Mar 2022, Published online: 29 Aug 2022

Figures & data

Figure 1. Latest WHO guidelines on the clinical presentation of severe malaria [Citation18].

Hb: hemoglobin, Hct: hematocrit, PD: parasite density, SBP: systolic blood pressure.
*These signs/symptoms are variably defined with respect to the Plasmodium species.
Definitions of Po and Pm severe malaria are not stated in the guidelines.
Figure 1. Latest WHO guidelines on the clinical presentation of severe malaria [Citation18].

Figure 2A. Worldwide burden of severe malaria.

(1-4) In-hospital-attended patients due to Plasmodium spp species.
(1-4) P. falciparum mono-infections.
(1-4) P. vivax mono-infections in African and Eastern Mediterranean regions (A1, B1, C1), The Americas region (A2, B2, C2), South East Asia and West Pacific regions (A3, B3, C3), and India (A4, B4, C4), 2010—2020.
In (1-4), the total number of SM cases irrespective of the malaria species identified in studies was used to determine the proportion of SM in a country or an area.
Weighted proportions were calculated to depict hospital-admitted SM. For a same country or area, the weight of a study was determined by dividing the sample size of admitted patients in this study by the total number of admitted patients of all studies conducted in the country or area. The overall estimate of the SM proportion was then computed by summing up the product of SM prevalence in each study and the weight of the study.
LDR-FMA: ligase detection reaction-fluorescent microsphere assay, LM: light microscopy, PCR: polymerase chain reaction, QBC: quantitative buffy coat, RDT: rapid diagnostic test.
The maps were generated using the QGIS software.
The data are sourced from research articles retrieved from PubMed (https://pubmed.ncbi.nlm.nih.gov/), published in English and French languages We excluded studies with a small sample size, case—control design, published before 2010, imported malaria, studies with no country-wise data, and reviews/abstracts/book chapters.
Figure 2A. Worldwide burden of severe malaria.

Figure 2B. Continued.

Figure 2B. Continued.

Figure 2C. Continued.

Figure 2C. Continued.

Figure 3A. Continued.

Figure 3A. Continued.

Figure 3B. Importance (A) and categorization (B) of biomarkers in malaria infection.

Figure 3B. Importance (A) and categorization (B) of biomarkers in malaria infection.

Figure 4. Clinical biomarkers and severe malaria.

Origin of biomarkers.
Biological nature of biomarkers by malaria species.
Type of biomarkers evaluated.
Biomarkers for which reporting AUC/sensitivity/specificity values were included.
In 4B, the numbers above the histograms represent the total number of evaluated biomarkers for each malaria species.
In 4C, some biomarkers were studied in more than one malaria species (e.g. the platelet count was evaluated as biomarkers of severe malaria with Pf, Pv, or Pk).
AHSG: alpha-2-Heremans-Schmid glycoprotein, AUC: area under the curve, CAMs: cell adhesion molecules, CK-MB: cardiac disease creatine kinase muscle-brain type, CLI/CLU: circulatory complement‑lysis inhibitor or clusterin, DNA: deoxyribonucleic acid, eGRF: estimated glomerular filtration rate, HMGB1: high-mobility group box protein 1, HRP2: histidine-rich protein 2, IP-10: 10-kDa INF gamma-induced protein, Pf: Plasmodium falciparum, Pk: Plasmodium knowlesi, Pv: Plasmodium vivax, pLDH: parasite lactate dehydrogenase, RBC: red blood cell, sICAM-1: soluble intercellular cell adhesion molecule 1, SERPINA3: serpin peptidase inhibitor clade A member 3, sTie-2: soluble cognate receptor, sTREM 1: soluble triggering receptor expressed on myeloid cells 1, suPAR: soluble urokinase-type plasminogen activator, TNF: tumour necrosis factor, VEGF: vascular endothelial growth factor, vWF: von Willebrand factor.
Figure 4. Clinical biomarkers and severe malaria.

Figure 5. Biomarkers evaluated for severe Pf-SM.

Biomarkers for AUC/sensitivity/specificity values were included.
Biomarkers for severe Pf malaria were categorized into three groups: i) suboptimal [the biomarker has AUC and/or sensitivity and specificity values below thresholds], ii) Good+ [the biomarker has AUC and/or sensitivity and specificity values above thresholds, but with limited evidence of its clinical utility due to several reasons, including a low number of studies (n = 1), no statistical significance was provided for AUC, evaluated in a specific population (i.e. nonimmune European travelers and evaluated by the same research team], and iii) Good++ [the biomarker has AUC and/or sensitivity and specificity values above thresholds, and strong evidence of its clinical utility due to several reasons including number of studies > 1, statistical significance provided for AUC, evaluated in different malaria endemic settings, evaluated in populations from malaria endemic areas, and evaluated by different research teams]. The Ang-2 and Ang-2/1 ratio (yellow star) showed good prognosis performances for most of the SM-related outcomes.
Ang: angiopoietin, CCL: chemokine ligand, cfDNA: cell-free deoxyribonucleic acid, CHI3L1: chitinase-3-like 1 protein, CRP: C-reactive protein, DNA: deoxyribonucleic acid, HMGB1: high-mobility group box protein 1, HRP2: histidine-rich protein 2, IFN: interferon, IL: interleukins, IP-10: 10-kDa INF gamma-induced protein, PC: platelet count, PCN: pigment-containing cells, PCT: procalcitonin, Pf: Plasmodium falciparum, PWD: platelet width distribution, RBCDW: red blood cell distribution width, RP: retinopathy positive, sICAM-1: soluble intercellular cell adhesion molecule 1, sTie-2: soluble cognate receptor, suPAR: soluble urokinase-type plasminogen activator, TNF: tumor necrosis factor, VCAM-1: vascular adhesion molecule 1, VEGF: vascular endothelial growth factor, vWF: von Willebrand factor.
Figure 5. Biomarkers evaluated for severe Pf-SM.

Figure 6. Biomarkers evaluated for severe Pv and Pk malaria.

Biomarkers for AUC/sensitivity/specificity values were included.
Biomarkers for severe Pv and Pk malaria were categorized into three groups: i) suboptimal [the biomarker has AUC and/or sensitivity and specificity values below thresholds], ii) Good+ [the biomarker has AUC and/or sensitivity and specificity values above thresholds, but with limited evidence of its clinical utility due to several reasons including a low number of studies (n = 1), no statistical significance was provided for AUC, evaluated in a specific population (i.e. nonimmune European travelers, and evaluated by the same research team], iii) Good++ [the biomarker has AUC and/or sensitivity and specificity values above thresholds, and strong evidence of its clinical utility due to several reasons including number of studies > 1, statistical significance provided for AUC, evaluated in different malaria endemic settings, evaluated in populations from malaria endemic areas, and evaluated by different research teams].
Ang: angiopoietin, CRP: C-reactive protein, IFN: interferon, IL: Interleukin, Pk: Plasmodium knowlesi, Pv: Plasmodium vivax, sICAM-1: soluble intercellular cell adhesion molecule 1, SOD-1: superoxide dismutase 1, TNF-α: tumor necrosis factor alpha, VCAM-1: vascular adhesion molecule 1.
Figure 6. Biomarkers evaluated for severe Pv and Pk malaria.

Table 1. Comparison of quantitative methods of SM biomarkers.

Figure 7. Utility of omics technologies to accelerate the identification and validation of clinical biomarkers for SM.

(a) Blood samples are collected from a small number of individuals based on symptomatology of malaria (i.e. no malaria/healthy control, uncomplicated malaria, and severe malaria). These different clinical groups are defined based on the objectives of the study. (b) Samples are processed using omics technologies to identify RNA/protein/peptide expression patterns between these different clinical groups in order to identify differentially expressed molecules. (c) The most differentially expressed biomolecules are identified and characterized (d) Case–control studies may be implemented to evaluate the clinical value of these sets of selected biomolecules. Parameters such as sensitivity, specificity, and area under the curve can be evaluated. Other aspects such as reliability, simplicity, and practicality of the measurement method can be evaluated in parallel. In this scenario, three clinical groups were used as an example, but design could be made more complex by defining or adding supplementary clinical groups (e.g. i—severe Pf + Pv cases, ii—different severe malaria forms as cerebral malaria, severe malarial anemia, respiratory distress, iii—severe co-infections with Plasmodium and other pathogens – virus, bacteria, etc.) and then performing omics technologies as presented in the figure, and (e) the development of a rapid, reliable, and affordable assay system should be the final step for the utilization of clinical biomarkers.
Figure 7. Utility of omics technologies to accelerate the identification and validation of clinical biomarkers for SM.

Table 2. Some proposed severe malaria biomarkers for which clinical performances were not evaluated.

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Data availability statement

All data included in this study are fully available from the corresponding author on reasonable request.