A unique volatile signature distinguishes malaria infection from other conditions that cause similar symptoms.

Recent findings suggest that changes in human odors caused by malaria infection have significant potential as diagnostic biomarkers. However, uncertainty remains regarding the specificity of such biomarkers, particularly in populations where many different pathological conditions may elicit similar symptoms. We explored the ability of volatile biomarkers to predict malaria infection status in Kenyan schoolchildren exhibiting a range of malaria-like symptoms. Using genetic algorithm models to explore data from skin volatile collections, we were able to identify malaria infection with 100% accuracy among children with fever and 75% accuracy among children with other symptoms. While we observed characteristic changes in volatile patterns driven by symptomatology, our models also identified malaria-specific biomarkers with robust predictive capability even in the presence of other pathogens that elicit similar symptoms.