Can we use human odors to diagnose malaria?

• Published in: Future microbiology 2019-01, Vol.14 (1), p.5-9
• Main Authors: Stanczyk, Nina M, De Moraes, Consuelo M, Mescher, Mark C
• Format: Article
• Language: English
• Subjects: Aldehydes - analysis; Alkanes - analysis; Animal models; Animals; Bioindicators; Biomarkers; Biomarkers - analysis; Clinical trials; diagnostics; Disease; disease biomarkers; Equipment Design; Hexanal; Humans; Infections; Lung cancer; Lung diseases; Malaria; Malaria - diagnosis; Mice; Microscopy; Mosquitoes; Odorants - analysis; Odors; Parasites; Pathogens; Plasmodium; Plasmodium malariae - metabolism; Prediction models; Skin; Skin - metabolism; Studies; Toluene; Toluene - analysis; Vector-borne diseases; Volatile Organic Compounds - analysis; volatiles
• ISSN: 1746-0913; 1746-0921
• DOI: 10.2217/fmb-2018-0312

Taking a broader view, it is promising that each of these recent studies -which employed diverse collection and analysis techniques and examined individuals from clinical trials as well as field populations - found differences in volatile emissions associated with malaria infection status while those employing predictive models reported an ability to identify malaria-infected individuals with relatively high sensitivity (4,13,15-17). Volatile compounds that have so far been identified as important or predictive of malarial status in multiple studies, such as nonanal emitted from human skin and breath (4,15,16), hexanal from human skin and Plasmodium in vitro (4,19), toluene in human skin and Plasmodium in vitro (4,18) and tridecane from human breath and whole-body collections from mice (10,16), are promising candidates for use in diagnostic devices, and all of these compounds except toluene have also been shown to elicit mosquito responses (4,10). Overcoming the first challenge will entail the precise characterization of potential volatile biomarkers identified by recent and future studies, and determining whether they are sufficiently robust to face off broad-scale interindividual variation. Since no study to date has identified a single compound whose levels reliably predict infection status, any diagnostic method will likely have to assess the presence of a more complex disease signature involving several compounds. Sensors within these arrays are capable of recognizing compounds according to various properties (mass, electrical, electron/photon interaction), and connect to an electronic circuit able to classify samples according to patterns across compounds, matching them to previously inputted uninfected and infected ‘fingerprints’ to determine disease status in lung cancer and other diseases producing specific volatile compounds (7).