Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat

Abstract

Metabolic syndrome conditions, diabetes, prediabetes, and non-alcoholic fatty liver disease (NAFLD) can be observed by monitoring the metabolic biomarkers glucose and galactose. These biomarkers modulate dynamically within human physiology, mainly based on nutritional intake. Traditionally, these biomarkers are measured in blood, which does not lend itself to dynamic monitoring. TAGG is a sweat-based electrochemical platform developed for continuous tracking. This work is the first demonstration of a point-of-care, non-invasive design to detect the dynamic interplay between two biomarkers, glucose and galactose, in human sweat. The TAGG platform detection range for both glucose and galactose is 0.05–32 mg/dL and 0.05 mg/dL limit of detection. Sweat samples were collected from four human subjects. The glucose and galactose data strongly correlated (r = 0.9864 and r = 0.9641) with ELISA standard reference method. By monitoring the dynamics of these metabolic biomarkers, we can gain greater insight into the complex interactions between nutrition and metabolic syndrome. This work demonstrates proof of concept of the non-invasive TAGG detection platform for tracking glucose and galactose dynamics in the low, high, and normal physiological ranges in human sweat.