A new paradigm in tracking the dynamics of glucose and cortisol: An observational study from human sweat enabled by a skin sensor

• Published in: Biosensors and bioelectronics. X Vol. 14; p. 100377
• Main Authors: Sardesai, Abha Umesh, Greyling, Cornelia Felicia, Lin, Kai-Chun, Kumar, Ruchita Mahesh, Muthukumar, Sriram, Prasad, Shalini
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023; Elsevier
• Subjects: Cortisol; electrochemical sensor; Glucose; Sweat sensor; Sweat sensor; Cortisol; electrochemical sensor; Glucose
• ISSN: 2590-1370; 2590-1370
• DOI: 10.1016/j.biosx.2023.100377

A sedentary lifestyle is one of the leading risk factors for developing obesity, increasing the risk of cardiovascular disease. In this work, we developed a novel approach to tracking glucose and cortisol to observe the dynamic relationship between the target biomarkers due to physical activity, nutrition, and the circadian cycle, via a machine learning-assisted wearable sweat-based electrochemical sensor. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sensing platform by examining the glucose and cortisol levels in passive sweat (<1 μL/min) of 4 healthy participants over 3 weeks for approximately 24–30 h twice a week. The ensemble learning model reached an R2 of 0.96 with an RMSE of 0.4. This novel glucose and cortisol tracking paradigm establishes the foundation for future research, including lifestyle choices’ effect on target biomarkers of metabolic disorders. •Non-invasive and simultaneous tracking of dynamics of glucose and cortisol in passive human sweat.•Machine learning-based reporting of affinity electrochemical detection throughout the circadian cycle.•Observational study of the change in glucose and cortisol levels of 4 healthy subjects as.