| Title: | Volatile biomarkers of symptomatic and asymptomatic malaria infection in humans |
| Author(s): | De Moraes CM; Wanjiku C; Stanczyk NM; Pulido H; Sims JW; Betz HS; Read AF; Torto B; Mescher MC; |
| Address: | "Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland. Behavioural and Chemical Ecology Unit, International Centre of Insect Physiology and Ecology, Nairobi, Kenya. Department of Biology, Pennsylvania State University, University Park, PA 16802. Department of Entomology, Pennsylvania State University, University Park, PA 16802. Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; mescher@usys.ethz.ch" |
| ISSN/ISBN: | 1091-6490 (Electronic) 0027-8424 (Print) 0027-8424 (Linking) |
| 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: | "Animals Biomarkers/*analysis/metabolism Child Discriminant Analysis Humans Kenya Machine Learning Malaria/*diagnosis/metabolism Models, Statistical Predictive Value of Tests Skin/*metabolism Volatile Organic Compounds/*analysis/metabolism asymptomatic inf;" |
| Notes: | "MedlineDe Moraes, Consuelo M Wanjiku, Caroline Stanczyk, Nina M Pulido, Hannier Sims, James W Betz, Heike S Read, Andrew F Torto, Baldwyn Mescher, Mark C eng Research Support, Non-U.S. Gov't 2018/05/16 Proc Natl Acad Sci U S A. 2018 May 29; 115(22):5780-5785. doi: 10.1073/pnas.1801512115. Epub 2018 May 14" |
