Science

Smells like malaria: Study finds body odor markers can identify infection

Smells like malaria: Study finds body odor markers can identify infection
This new method can identify those infected with malaria even when they are not displaying visible symptoms
This new method can identify those infected with malaria even when they are not displaying visible symptoms
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This new method can identify those infected with malaria even when they are not displaying visible symptoms
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This new method can identify those infected with malaria even when they are not displaying visible symptoms

Diagnosing symptomatic carriers of malaria is difficult enough, needing careful examination of blood samples to identify parasites, but picking up asymptomatic carriers is even more challenging. These infected carriers often bear no clinical symptoms yet act as reservoirs for the disease helping it spread to more and more people. An international team of researchers set out to find a way to diagnose asymptomatic malaria carriers and the key turned out to be in body odor.

"Our previous work in a mouse model found that malaria infection altered the odors of infected mice in ways that made them more attractive to mosquitoes, particularly at a stage of infection where the transmissible stage of the parasite was present at high levels," explains Consuelo De Moraes, a researcher working on the study.

The new study aimed to uncover what odor-based biomarkers could be detected in humans with either symptomatic or asymptomatic malaria infections. The researchers gathered skin and blood samples from over 400 primary school children across known malaria hot-spots in Western Kenya. Due to the limited sensitivity of identifying malaria using microscopy, the study also confirmed infections through DNA tests.

Skin volatility profiles were then constructed for each individual using gas chromatography-mass spectrometry. This allowed the researchers to clearly determine odor profiles based on skin volatility, comparing those subjects that were malaria-free to those with either symptomatic or asymptomatic infections.

"It is interesting that the symptomatic and asymptomatic infections were different from each other as well as from healthy people," said Mark C. Mescher, another scientist working on the project.

This data was then used to creative predictive models using machine learning to reliably identify human subjects infected with the disease. The robust results showed that the models could identify asymptomatic infections with 100 percent sensitivity. These models could accurately pick up low-level malarial infections that regular microscopy tests could not.

The team is pragmatic about how much work is ahead of them before an odor-based diagnostic test is practically available. A key hurdle at this stage is developing a cheaply deployable system that can determine these volatility profiles with high sensitivity under field conditions.

"In the near term, our goal is to refine the current findings to find the most reliable and effective biomarkers we can," says Mescher. "This is really basic science to identify the biomarkers of malaria. There is still a lot more work to be done to develop a practical diagnostic assay."

This isn't the first study to examine volatile biomarkers that could be used to diagnose malaria. In 2015 an Australian team published a study examining several compounds traced in human breath that could be positively correlated with a malaria infection. These volatile organic compounds in human breath may not be as sensitive to asymptomatic malarial infections as the ones identified in this new research but the primary hurdle faced by the earlier research is the same one still faced today.

Both studies are hamstrung by the need for elaborate gas chromatography-mass spectrometry devices to detect the target compounds. Further research into sensitive and inexpensive biosensors that can make these measurements in the field is necessary to bring these diagnostic tools into clinical practice.

The new study was published in the journal PNAS.

Source: Penn State

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