4.6 Article

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

期刊

PLOS ONE
卷 15, 期 3, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0223461

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资金

  1. CAPES/CNPq [458143/2014]
  2. FAPEMIG [APQ-02872-16]
  3. Federal University of Uberlandia
  4. National Institute of Science and Technology in Theranostics and Nanobiotechnology (CNPq) [465669/2014-0]
  5. Canadian Institutes of Health Research (CIHR) [106657, 400347]
  6. FAPEMIG
  7. CNPq
  8. CAPES
  9. PrInt CAPES/UFU

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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 (R-2 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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