Non-invasive platform to estimate fasting blood glucose levels from salivary electrochemical parameters

• Published in: Healthcare technology letters Vol. 6; no. 4; pp. 87 - 91
• Main Authors: Malik, Sarul, Parikh, Harsh, Shah, Neil, Anand, Sneh, Gupta, Shalini
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01.08.2019; John Wiley & Sons, Inc; Wiley
• Subjects: Age; Algorithms; blood; Ca2; Ca^2; calcium; Diabetes; diseases; electrochemical properties; electrochemical sensors; fasting blood glucose levels; FBGL determination process; Females; Glucose; graphical user interfaces; Hyperglycemia; ionic concentrations; K; medical computing; metabolic disorder; Na; Neural networks; noninvasive platform; Oxidation; painless FBGL estimation; patient diagnosis; pH measurement; portable sensors; Potassium; proxy biofluid; salivary electrochemical parameters; Sensors; sodium; sugar; Volunteers; potassium; graphical user interfaces; painless FBGL estimation; K; proxy biofluid; fasting blood glucose levels; metabolic disorder; calcium; salivary electrochemical parameters; electrochemical properties; diseases; portable sensors; blood; sodium; ionic concentrations; electrochemical sensors; Ca2; noninvasive platform; FBGL determination process; Na+; sugar; pH measurement; medical computing; diabetes; patient diagnosis
• ISSN: 2053-3713; 2053-3713
• DOI: 10.1049/htl.2018.5081

Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K+, Na+ and Ca2+ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% (R2 = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors’ algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% (R2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.