Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat
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 bi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Biosensors and Bioelectronics: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590137023000547 |
| _version_ | 1797733901517455360 |
|---|---|
| author | Cornelia Felicia Greyling Sasya Madhurantakam Kai-Chun Lin Sriram Muthukumar Shalini Prasad |
| author_facet | Cornelia Felicia Greyling Sasya Madhurantakam Kai-Chun Lin Sriram Muthukumar Shalini Prasad |
| author_sort | Cornelia Felicia Greyling |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T12:35:22Z |
| format | Article |
| id | doaj.art-850ae7269d434b1483d8b6911d72bea4 |
| institution | Directory Open Access Journal |
| issn | 2590-1370 |
| language | English |
| last_indexed | 2024-03-12T12:35:22Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biosensors and Bioelectronics: X |
| spelling | doaj.art-850ae7269d434b1483d8b6911d72bea42023-08-29T04:17:51ZengElsevierBiosensors and Bioelectronics: X2590-13702023-09-0114100357Tracking Active Glucose-Galactose [TAGG] dynamics in human sweatCornelia Felicia Greyling0Sasya Madhurantakam1Kai-Chun Lin2Sriram Muthukumar3Shalini Prasad4Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USADepartment of Bioengineering, University of Texas at Dallas, Richardson, TX, USADepartment of Bioengineering, University of Texas at Dallas, Richardson, TX, USAEnLiSense LLC, Allen, TX, USADepartment of Bioengineering, University of Texas at Dallas, Richardson, TX, USA; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2590137023000547GlucoseGalactoseSweat-based sensorBiosensor |
| spellingShingle | Cornelia Felicia Greyling Sasya Madhurantakam Kai-Chun Lin Sriram Muthukumar Shalini Prasad Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat Biosensors and Bioelectronics: X Glucose Galactose Sweat-based sensor Biosensor |
| title | Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat |
| title_full | Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat |
| title_fullStr | Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat |
| title_full_unstemmed | Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat |
| title_short | Tracking Active Glucose-Galactose [TAGG] dynamics in human sweat |
| title_sort | tracking active glucose galactose tagg dynamics in human sweat |
| topic | Glucose Galactose Sweat-based sensor Biosensor |
| url | http://www.sciencedirect.com/science/article/pii/S2590137023000547 |
| work_keys_str_mv | AT corneliafeliciagreyling trackingactiveglucosegalactosetaggdynamicsinhumansweat AT sasyamadhurantakam trackingactiveglucosegalactosetaggdynamicsinhumansweat AT kaichunlin trackingactiveglucosegalactosetaggdynamicsinhumansweat AT srirammuthukumar trackingactiveglucosegalactosetaggdynamicsinhumansweat AT shaliniprasad trackingactiveglucosegalactosetaggdynamicsinhumansweat |