Earwax, a possible ally for the early detection of Parkinson's

Earwax could be a simple and inexpensive future Parkinson's detection system, according to a study by Chinese researchers who have developed a system that detects the disease based on the smell of earwax using artificial intelligence.

Early intervention for this degenerative neurological disease is critical to optimizing care. Most treatments only slow its progression, making early diagnosis essential. However, current tests, such as clinical assessment scales and neuroimaging, can be subjective and expensive.

Previous research has shown that changes in earwax, an oily substance secreted by the skin, may help identify people with Parkinson's . Specifically, the odor of people with the disease may have a distinctive odor because the volatile organic compounds (VOCs) released by earwax are altered by disease progression, including neurodegeneration, systemic inflammation, and oxidative stress.

When skin sebum is exposed to environmental factors such as air pollution and humidity, its composition can change, making it an unreliable testing medium. However, the skin inside the ear canal remains protected from the elements. Therefore, researchers Hao Dong of the School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, and Danhua Zhu of Zhejiang University, and their colleagues wanted to focus their Parkinson's detection efforts on earwax, which is composed primarily of sebum and is easy to sample.

To identify potential disease-related VOCs in earwax, the researchers swabbed the ear canals of 209 people (108 of whom had been diagnosed). They analyzed the collected secretions using gas chromatography-mass spectrometry techniques. Four of the VOCs the researchers found in the earwax of people with Parkinson's were significantly different from the earwax of people without the disease. They concluded that these four VOCs, including ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane, are potential biomarkers of the disease.

Dong, Zhu, and their colleagues then trained an artificial intelligence olfactory system (AIOS) with their earwax VOC data. The resulting AIOS-based screening model categorized earwax samples from people with and without Parkinson's disease with 94% accuracy. According to the researchers, the AIOS system could be used as a first-line screening tool for the early detection of PD and pave the way for early medical intervention, thereby improving patient care.

"This method is a small-scale experiment at a single center in China," Dong said. "The next step is to conduct further research at different stages of the disease, across multiple research centers, and among multiple ethnic groups, to determine whether this method has greater practical application value."