Salivary molecular spectroscopy : a sustainable, rapid and non-invasive monitoring tool for diabetes mellitus during insulin treatment

Caixeta, Douglas C. and Aguiar, Emília M. G. and Cardoso-Sousa, Léia and Coelho, Líris M. D. and Oliveira, Stephanie W. and Espindola, Foued S. and Raniero, Leandro and Crosara, Karla T. B. and Baker, Matthew J. and Siqueira, Walter L. and Sabino-Silva, Robinson (2020) Salivary molecular spectroscopy : a sustainable, rapid and non-invasive monitoring tool for diabetes mellitus during insulin treatment. PLoS ONE, 15 (3). e0223461. ISSN 1932-6203 (https://doi.org/10.1371/journal.pone.0223461)

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Abstract

Monitoring of blood glucose is an invasive, painful and costly practice in diabetes. Consequently, the search for a more cost-effective (reagent-free), non-invasive and specific diabetes monitoring method is of great interest. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been used in diagnosis of several diseases, however, applications in the monitoring of diabetic treatment are just beginning to emerge. Here, we used ATR-FTIR spectroscopy to evaluate saliva of non-diabetic (ND), diabetic (D) and insulin-treated diabetic (D+I) rats to identify potential salivary biomarkers related to glucose monitoring. The spectrum of saliva of ND, D and D+I rats displayed several unique vibrational modes and from these, two vibrational modes were pre-validated as potential diagnostic biomarkers by ROC curve analysis with significant correlation with glycemia. Compared to the ND and D+I rats, classification of D rats was achieved with a sensitivity of 100%, and an average specificity of 93.33% and 100% using bands 1452 cm-1 and 836 cm-1, respectively. Moreover, 1452 cm-1 and 836 cm-1 spectral bands proved to be robust spectral biomarkers and highly correlated with glycemia (R2 of 0.801 and 0.788, P < 0.01, respectively). Both PCA-LDA and HCA classifications achieved an accuracy of 95.2%. Spectral salivary biomarkers discovered using univariate and multivariate analysis may provide a novel robust alternative for diabetes monitoring using a non-invasive and green technology.

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