Volatile biomarkers of symptomatic and asymptomatic malaria infection in humans

Abstract

Malaria remains among the world's deadliest diseases, and control efforts depend critically on the availability of effective diagnostic tools, particularly for the identification of asymptomatic infections, which play a key role in disease persistence and may account for most instances of transmission but often evade detection by current screening methods. Research on humans and in animal models has shown that infection by malaria parasites elicits changes in host odors that influence vector attraction, suggesting that such changes might yield robust biomarkers of infection status. Here we present findings based on extensive collections of skin volatiles from human populations with high rates of malaria infection in Kenya. We report broad and consistent effects of malaria infection on human volatile profiles, as well as significant divergence in the effects of symptomatic and asymptomatic infections. Furthermore, predictive models based on machine learning algorithms reliably determined infection status based on volatile biomarkers. Critically, our models identified asymptomatic infections with 100% sensitivity, even in the case of low-level infections not detectable by microscopy, far exceeding the performance of currently available rapid diagnostic tests in this regard. We also identified a set of individual compounds that emerged as consistently important predictors of infection status. These findings suggest that volatile biomarkers may have significant potential for the development of a robust, noninvasive screening method for detecting malaria infections under field conditions.

Keywords: asymptomatic infection; diagnostics; disease biomarkers; malaria; volatiles.

Fig. 1.

Volatile samples were collected from primary school children at 41 schools in western Kenya.

Fig. 2.

Group separation using DAPC reveals differences between malaria-infected (asymptomatic + symptomatic) and uninfected individuals in foot and arm volatiles for datasets K1 and K2. Vertical lines beneath the x-axis represent individual samples.

Fig. 3.

Group separation using DAPC for K2 arm and foot volatiles. (Top) Differences among uninfected individuals and individuals with symptomatic and asymptomatic malaria infections, confirmed by both microscopy and PCR. (Bottom) Differences among uninfected individuals and individuals with submicroscopic symptomatic and asymptomatic infections, detected only by PCR. Points represent individual samples, with colors denoting malaria condition and inclusion of 95% inertia ellipses.

Fig. 4.

(Top) Heatmap showing the fold-change of individual compounds in asymptomatic and symptomatic individuals relative to those in uninfected individuals for K2-arm and K2-foot. (Bottom) Volcano plots showing changes in individual compounds in asymptomatic and symptomatic individuals, relative to those in uninfected individuals, with compounds significantly up- or down-regulated shown in green (P < 0.05 and absolute fold change >1.5). Nonsignificant regulated compounds with absolute fold change <1.5 shown in black. (Compound IDs are listed in Table 2.)