
Non‐invasive platform to estimate fasting blood glucose levels from salivary electrochemical parameters
Author(s) -
Malik Sarul,
Parikh Harsh,
Shah Neil,
Anand Sneh,
Gupta Shalini
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5081
Subject(s) - saliva , computer science , proxy (statistics) , algorithm , machine learning , chemistry , biochemistry
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 Ca 2+ 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% ( R 2 = 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% ( R 2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.