Summary: A novel screening method for Parkinson’s disease (PD) analyzes the volatile compounds in ear wax to detect early signs of the condition. Researchers found that four specific volatile organic compounds (VOCs) are significantly different in people with PD.
Using this information, they developed an AI-powered olfactory system that distinguished between PD and non-PD samples with 94% accuracy. This inexpensive, non-invasive technique could revolutionize early detection and treatment strategies.
Key Facts:
- Biomarker Discovery: Four volatile compounds in ear wax were found to differ significantly in people with Parkinson’s.
- AI Accuracy: An artificial intelligence olfactory system achieved 94% accuracy in classifying PD vs non-PD samples.
- Non-Invasive Test: Ear wax is a protected and stable source of sebum-based biomarkers, offering a reliable testing medium.
Source: ACS
Most treatments for Parkinson’s disease (PD) only slow disease progression. Early intervention for the neurological disease that worsens over time is therefore critical to optimize care, but that requires early diagnosis.
Current tests, like clinical rating scales and neural imaging, can be subjective and costly.
Now, researchers in ACS’ Analytical Chemistry report the initial development of a system that inexpensively screens for PD from the odors in a person’s ear wax.
Previous research has shown that changes in sebum, an oily substance secreted by the skin, could help identify people with PD. Specifically, sebum from people with PD may have a characteristic smell because volatile organic compounds (VOCs) released by sebum are altered by disease progression — including neurodegeneration, systemic inflammation and oxidative stress.
However, when sebum on the skin is exposed to environmental factors like air pollution and humidity, its composition can be altered, making it an unreliable testing medium. But the skin inside the ear canal is kept away from the elements.
So, Hao Dong, Danhua Zhu and colleagues wanted to focus their PD screening efforts on ear wax, which mostly consists of sebum and is easily sampled.
To identify potential VOCs related to PD in ear wax, the researchers swabbed the ear canals of 209 human subjects (108 of whom were diagnosed with PD). They analyzed the collected secretions using gas chromatography and mass spectrometry techniques.
Four of the VOCs the researchers found in ear wax from people with PD were significantly different than the ear wax from people without the disease. They concluded that these four VOCs, including ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane, are potential biomarkers for PD.
Dong, Zhu and colleagues then trained an artificial intelligence olfactory (AIO) system with their ear wax VOC data. The resulting AIO-based screening model categorized with 94% accuracy ear wax samples from people with and without PD.
The AIO system, the researchers say, could be used as a first-line screening tool for early PD detection and could pave the way for early medical intervention, thereby improving patient care.
“This method is a small-scale single-center experiment in China,” says Dong.
“The next step is to conduct further research at different stages of the disease, in multiple research centers and among multiple ethnic groups, in order to determine whether this method has greater practical application value.”
Funding: The authors acknowledge funding from the National Natural Sciences Foundation of Science, Pioneer and Leading Goose R&D Program of Zhejiang Province, and the Fundamental Research Funds for the Central Universities.
About this Parkinson’s disease research news
Author: Emily Abbott
Source: ACS
Contact: Emily Abbott – ACS
Image: The image is credited to Neuroscience News
Original Research: Open access.
“An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson’s Disease Using Volatile Organic Compounds from Ear Canal Secretions” by Danhua Zhu et al. Analytical Chemistry
Abstract
An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson’s Disease Using Volatile Organic Compounds from Ear Canal Secretions
Parkinson’s Disease (PD), a frequently diagnosed neurodegenerative condition, poses a major global challenge. Early diagnosis and intervention are crucial for PD treatment.
This study proposes a diagnostic model for PD that analyzes volatile organic compounds (VOCs) from ear canal secretions (ECS).
Using gas chromatography–mass spectrometry (GC-MS) to examine ECS samples from patients, four VOC components (ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane) were identified as biomarkers with statistically significant differences between PD and non-PD patients.
Diagnostic models based on these VOC components demonstrate strong capability in identifying and classifying PD patients.
To enhance the accuracy and efficiency of the PD diagnostic model, this study introduces a protocol for extracting features from chromatographic data.
By integrating gas chromatography–surface acoustic wave sensors (GC-SAW) with a convolutional neural network (CNN) model, the system achieves an accuracy of up to 94.4%.
Further enhancements to the diagnostic model could pave the way for a promising new PD diagnostic solution and the clinical use of a bedside PD diagnostic device.