Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes

Area of Science:

• Biomedical Engineering
• Medical Diagnostics
• Physiology

Background:

• Microvascular dysfunction is linked to diabetes and aging.
• Early detection of microvascular changes is crucial for managing these conditions.
• Non-invasive monitoring of microcirculation is needed.

Purpose of the Study:

• To evaluate a wireless dynamic light scattering sensor for detecting microvascular changes.
• To assess the diagnostic value of signal processing techniques for classifying individuals based on vascular health.
• To apply machine learning for distinguishing between young healthy, elderly healthy, and type 2 diabetic individuals.

Main Methods:

• Utilized wireless portable dynamic light scattering sensors with laser Doppler flowmetry.
• Analyzed blood perfusion time series using continuous wavelet transform and autocorrelation.
• Employed machine learning algorithms for group classification.

Main Results:

• The sensor successfully detected microvascular changes in volunteers.
• Continuous wavelet spectrum analysis showed significant diagnostic value for type 2 diabetes.
• Reduced normalized autocorrelation function observed in elderly and diabetic groups compared to young controls.
• Machine learning algorithms achieved effective classification of the studied groups.

Conclusions:

• Wireless dynamic light scattering sensors are effective for assessing microvascular health.
• Signal processing techniques, including wavelet analysis and autocorrelation, provide valuable diagnostic information.
• Machine learning can accurately classify individuals based on microcirculatory parameters, aiding in the detection of diabetes and aging-related changes.