A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance.

Area of Science:

• Biomedical Engineering
• Nutritional Science
• Medical Diagnostics

Background:

• Vitamin D deficiency and excess lead to various health issues requiring management.
• Accurate, non-invasive methods for measuring serum vitamin D levels are currently lacking.
• Continuous monitoring of vitamin D is crucial for preventing related diseases.

Purpose of the Study:

• To investigate correlations between vitamin D levels, body composition (InBody scan), and blood parameters.
• To determine optimal impedance frequencies for vitamin D quantification.
• To propose a novel, impedance-based method for predicting vitamin D concentration.

Main Methods:

• Assessed body composition and arm impedance using InBody device.
• Analyzed blood parameters including albumin and lactate dehydrogenase.
• Utilized machine learning algorithms to correlate impedance values with blood vitamin D concentrations.
• Developed a predictive algorithm for vitamin D levels based on impedance measurements.

Main Results:

• Body fat percentage, albumin, and lactate dehydrogenase showed significant correlation with vitamin D levels.
• An impedance frequency of 21.1 Hz demonstrated an optimal reflection of blood vitamin D concentration.
• The developed algorithm achieved approximately 75% confidence in predicting in-body vitamin D levels.

Conclusions:

• Impedance measurement values can effectively predict vitamin D concentration in the body.
• This non-invasive method offers potential for predicting and monitoring vitamin D-related diseases.
• The impedance-based approach may be integrated into wearable health devices for continuous monitoring.