Abstract
Malaria is a major international public health problem that affects millions of patients worldwide especially in sub-Saharan Africa. Although many tests have been developed to diagnose malaria infections, we still lack reliable diagnostic biomarkers for the identification of disease severity, especially in endemic areas where the diagnosis of cerebral malaria is very difficult and requires the exclusion of all other possible causes. Previous host and pathogen transcriptomic studies have not yielded homogenous results that can be harnessed into a reliable diagnostic tool. Here we utilized a multi-cohort analysis approach using machine-learning algorithms to identify blood gene signatures that can distinguish severe and cerebral malaria from moderate and non-cerebral cases. Using a Regularized Random Forest model, we identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria, respectively. We tested the specificity of both signatures against other common infectious diseases to ensure the signatures reliability and suitability as diagnostic markers. The severe and cerebral malaria gene-signatures were further integrated through k-top scoring pairs classifiers into ten and nine gene pairs that could distinguish severe and cerebral malaria, respectively. These signatures have various implications that can be utilized as blood diagnostic tools for malaria severity in endemic countries.
Highlights
Malaria is an important vector-transmitted infectious disease that affect millions of patients worldwide especially in sub-Saharan Africa, with an estimated new 228 million cases and 405,000 deaths in 2018 alone (World Malaria Report, 2019)
We evaluated both signatures on the unseen testing data using different performance metrics including the area under the ROC curve (AUC) and the area under the precision recall curve (AUPRC)
The available diagnostic tools lack a reliable and accessible measure to distinguish severe and cerebral malaria from mild cases, especially in high endemicity areas, where the identification of other infections can be confused with malaria asymptomatic parasitemia
Summary
Malaria is an important vector-transmitted infectious disease that affect millions of patients worldwide especially in sub-Saharan Africa, with an estimated new 228 million cases and 405,000 deaths in 2018 alone (World Malaria Report, 2019). Despite the decreasing number of new patients, a result of multinational efforts, and various advancements in diagnosis and treatment options, it is still a large burden especially on the countries most affected. Many diagnostic tests have been developed for the identification and screening of malaria infections (McMorrow et al, 2011), and some clinical signs such as retinopathy are hypothesized to be associated with severe and cerebral malaria (Beare et al, 2006), we still lack a reliable diagnostic biomarker for the identification of disease severity. More sensitive diagnostic and prognostic tools are required to enable rapid identification of severe and cerebral malaria to ensure adequate therapeutic response, which would improve disease outcome (Mwangi et al, 2005; Vinnemeier et al, 2012)
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