Non-Invasive Blood Cortisol Estimation from Sweat Analysis by Kinetic Modeling of Cortisol Transport Dynamics

Area of Science:

• Endocrinology
• Biomedical Engineering
• Physiological Monitoring

Background:

• Cortisol dysregulation is linked to Cushing's syndrome and stress disorders, impacting metabolic health and well-being.
• Current sweat-based cortisol monitoring is limited by moderate blood-sweat correlation (R<0.6), hindering clinical use.
• Non-invasive monitoring of cortisol is crucial for managing various health conditions.

Purpose of the Study:

• To develop and validate a novel kinetic model for estimating blood cortisol concentration from sweat cortisol measurements.
• To improve the accuracy of non-invasive cortisol monitoring using sweat-sensing technology.
• To enable personalized physiological parameter estimation for clinical decision-making.

Main Methods:

• Developed a kinetic model simulating cortisol transport dynamics from blood to sweat.
• Validated the model using sweat and blood cortisol data from 44 post-cardiac surgery patients.
• Assessed the correlation between model-estimated and experimentally measured blood cortisol concentrations.

Main Results:

• Achieved a high Pearson correlation coefficient of 0.95 (95% CI: 0.92-0.97) between estimated and experimental blood cortisol.
• The model accurately reflects patients' physiological status under diverse clinical conditions.
• Demonstrated personalized estimation of physiological parameters.

Conclusions:

• The novel kinetic model provides a highly accurate, non-invasive method for estimating blood cortisol from sweat.
• This technology facilitates long-term cortisol monitoring, crucial for conditions involving cortisol dysregulation.
• Personalized physiological parameter estimation supports clinical decision-making in managing endocrine disorders.