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

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 diabetic 6U-treated of insulin (D6U) rats to identify potential salivary biomarkers related to glucose monitoring. The spectrum of saliva of ND, D and D6U 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 D6U 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.

Dittrich, 2012). Considering that a biomolecule is determined by its unique structure, each 104 one will exhibit a unique ATR-FTIR spectrum, representing the vibrational modes of the 105 constituent structural bonds (Severcan et al., 2010; Ojeda e Dittrich, 2012). ATR-FTIR 106 is a green technology due to processes that eliminate the use of hazardous elements an 107 overarching approach that is applicable to monitoring diseases. The IR spectral modes of 108 biological samples, such as saliva, may be considered as biochemical fingerprints that 109 correlate directly with the presence or absence of diseases, and, furthermore, provide the

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In the present study, we tested the hypothesis that non-invasive spectral 117 biomarkers can be identified in saliva of hyperglycemic diabetic and in insulin-treated 118 diabetic rats, and the differentially expressed vibrational modes can be employed as 119 salivary biomarkers for diabetes monitoring. Thus, the aim of our study was to identify 120 infrared spectral signatures of saliva that are suitable to monitoring this metabolic disease 121 in untreated and insulin-treated conditions. For this, the salivary vibrational modes profile 122 of non-diabetic, diabetic and insulin-treated diabetic rats was quantitatively and 123 qualitatively evaluated using univariate and multivariate analysis.  128 To confirm the effectiveness of diabetes induction and insulin treatment, several 129 parameters were assessed in anesthetized animals. As expected, to confirm the diabetic 130 state, table 1 shows that diabetes reduced weight gain (p < 0.05), increased water intake 131 (p < 0.05) and food ingestion (p < 0.05) compared with ND rats. Besides, in diabetic 132 condition, higher plasma glucose (p < 0.05), as well as most pronounced urine volume (p 133 < 0.05), associated with higher urine glucose concentration (p < 0.05), were observed in 134 D rats compared with ND rats. Insulin treatment contributed to increased (p < 0.05) 5 135 weight gain and decreased water intake (p < 0.05) compared with placebo-treated D rats.

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As expected, insulin treatment decreased plasma glucose (p < 0.05), urine volume (p < 137 0.05) and urine glucose concentration compared with D rats. Glycemia and urine volume 138 were similar (p > 0.05) in ND and D6U animals, indicating that insulin treatment 139 completely reverted hyperglycemia and higher urine volume described in D rats. The   To investigate whether these salivary vibrational modes would be reflective of 165 glycemia regulation, these two salivary band areas were discovered to be, via univariate  Only one insulin-treated diabetic was categorized as non-diabetic. The total accuracy, 209 which is highly important for potential monitoring applications, was 95.2% (20/21). should ideally be possible to differentiate, therefore more studies need be investigated. 270 These results indicate that these spectral modes can be used as a diagnostic and 271 monitoring platform for diabetes mellitus, once interestingly, insulin treatment was also 272 able to revert the salivary spectra observed in hyperglycemic state. Therefore, insulin 273 treatment is not a potential confounding factor that may influence salivary vibrational  Cluster analyses confirm its potential to discriminate ND, D and D6U groups with 294 high accuracy. The success rate for ND e D was 100 %, and for D6U was 85.7%.  In conclusion, we showed that ATR-FTIR spectroscopy in saliva is able to 315 differentiate diabetic from non-diabetic and insulin-treated diabetic rats. Our data suggest

Chemical profile in stimulated saliva by ATR-FTIR Spectroscopy
Salivary spectra were recorded in 3000 cm -1 to 400 cm -1 region using ATR-FTIR 373 spectrophotometer Vertex 70 (Bruker Optics, Reinstetten, Germany) using a micro-374 attenuated total reflectance (ATR) component. The crystal material in ATR unit was a 375 diamond disc as internal-reflection element. The salivary pellicle penetration depth 376 ranges between 0.1 and 2 μm and depends on the wavelength, incidence angle of the beam 377 and the refractive index of ATR-crystal material. In the ATR-crystal the infrared beam is 378 reflected at the interface toward the sample. Saliva was directly dried using airflow on 379 ATR-crystal for 2 min before salivary spectra recorded. The air spectra was used as a 380 background in ATR-FTIR analysis. Sample spectra and background was taken with 4 cm -381 1 of resolution and 32 scans were performed for salivary analysis.

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Spectra data evaluation procedures 384 The spectra data obtained were processed using Opus 6.5 software (Bruker Optics, The quantity 1-specificity is the false positive rate and is the percentage of rats that are 400 incorrectly identified as diabetic (D).

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The sensitivity or true positive rate is defined as the percentage of rats who are correctly  410 The principal components were calculated using a full range of the FT-IR spectra 411 (ND, D and DU6) between 3700 and 500 cm-1, and a covariance matrix. The first step 412 was normalization followed by mean centering, the data were analyzed using the principal 413 components analysis (PCA). In this study, the first six principal components (PC1-PC6) 414 were used to perform the linear discriminant analysis (LDA) with leave-one-out cross-415 validation, according to the pathological reports.

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Infrared spectra of saliva samples were also analyzed by OPUS software (version 417 4.2) using hierarchical cluster analysis with first-derivative of the training data set. The