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article · Practical Laboratory Medicine

Diagnostic value of full blood count derived systemic inflammatory biomarkers in malaria infection

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Abstract

Background: Malaria remains a public health issue. Its associated inflammatory responses can easily shift from benefit to detriment, making early detection of malarial inflammation crucial. The full blood count promises to be a less expensive assay serving as a surrogate marker for inflammation. This study, therefore, aimed to determine the diagnostic value of FBC-derived systemic inflammatory biomarkers in malaria infection. Method: We employ a single point case-control design that included 45 malaria patients and 50 healthy individuals. We collected their anthropometric, sociodemographic, and clinical information. FBC estimation and malaria parasite enumeration were determined for each participant. Results: Malaria patients had higher values of all the systemic inflammatory biomarkers (monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and aggregate index of systemic inflammation (AISI)) compared with healthy individuals. There was significant moderate correlation between parasite count and NLR, and MLR (ρ = 0.5 and ρ = 0.4) and a weak negative correlation with PLR and AISI (ρ = - 0.2 each). A receiver operator characteristic (ROC) curve analysis showed that NLR (AUC = 0.937) had an excellent diagnostic and predictive value, with sensitivity of 86.7 % and specificity of 92.0 %. Conclusion: We have shown that the FBC-derived inflammatory biomarker- NLR- increases as parasite count increases. At a level of 2.12 and above, NLR is 86.7 % sensitive and 92.0 % specific in identifying the inflammatory state in malaria patients. Our findings show that the FBC-derived systemic inflammatory biomarkers provide a solution to the need for cost-effective surrogate inflammatory markers, especially in resource-deprived areas.

Research topics

  • Inflammatory Biomarkers in Disease Prognosis
  • Inflammation biomarkers and pathways
  • Statistical Methods in Epidemiology

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DOI: 10.1016/j.plabm.2025.e00494

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