Type 2 diabetes diagnosis assisted by machine learning techniques through the analysis of FTIR spectra of saliva

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.

Saliva may predict quality of life in psoriasis as measured by Fourier transform infrared spectroscopy (FTIR) and chemometrics

BACKGROUND

Psoriasis is a chronic skin disease, with several comorbidities, such as psoriatic arthritis, inflammatory bowel disease, metabolic syndrome, and impaired quality of life and work activity. The Dermatology Life Quality Index (DLQI) is the most commonly used quality of life index in psoriatic patients, as it is a marker of severe disease. This study evaluated the association between salivary Fourier transform Infrared Spectroscopy (FTIR) metabolic fingerprints and severity of psoriasis as measured by DLQI, using chemometric algorithms.

MATERIALS AND METHODS

Saliva was collected from 56 (27 with DLQI ≤ 10 [GI]; 29 with DLQI > 10 [GII]) psoriatic patients diagnosed and assessed by DLQI for disease severity by a dermatologist and analyzed by the transflectance technique in mid-infrared. Hierarchic cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and orthogonal partial least squares (OPLS) algorithms were used to associate salivary FTIR spectra with the respective DLQI scores.

RESULTS

Second derivative (2D) discriminated GI and GII at 2522 cm^-1 (p < 0.0001). HCA and PCA partially discriminated GI from GII at 4000-450 cm^-1 (p = 0.042 and 0.00821, respectively). Data processing with 1^st derivative (1D), 3 latent variables (LV) and 1 orthogonal signal correction (OSC) component at 2550-1801 cm^-1 generated an FTIR/OPLS-DA model with 100% accuracy to classify the severity of psoriasis, and an FTIR/OPLS model to quantify DLQI (range 0-28) with high performance: root mean square error of prediction (RMSEP) < 0.01 and coefficient of determination (R²) > 0.9999.

CONCLUSIONS

Salivary FTIR combined with chemometric algorithms such as OPLS-DA and OPLS can be used as a clinical tool to classify or predict the severity of psoriasis according to DLQI in patients with confirmed psoriasis.

Non/mini-invasive monitoring of diabetes-induced myocardial damage by Fourier transform infrared spectroscopy: Evidence from biofluids

Early identification of diabetic cardiomyopathy (DCM) can help clinicians develop targeted treatment plans and forensic pathologists make accurate postmortem diagnoses. In the present study, diabetes-induced metabolic abnormalities in the myocardium and biofluids (plasma, urine, and saliva) of db/db mice of various ages (7, 12, and 21 weeks) were investigated by attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results indicated that the diabetic and control groups had significantly different changes in the function groups of lipids, phosphate macromolecules (mostly nucleic acids), protein compositions and conformations, and carbohydrates (primarily glucose) in the myocardium and biofluids. The prediction model for quantifying DCM severity was developed on db/db mice's myocardial spectra using a genetic algorithm (GA)-partial least squares (PLS) regression method. Following that, the linear correlations between the predicted values for DCM severity and spectra for db/db biofluids were evaluated using the GA-PLS regression algorithm. The results showed there were good linear correlations between the predicted values for DCM severity and spectra for plasma (R² = 0.929), saliva (R² = 0.967), urine (R² = 0.954), and combination of plasma and saliva (R² = 0.980). This study provides a novel perspective on detecting diabetes-related biofluid and cardiac metabolic abnormalities and demonstrates the potential of biofluid infrared spectro-diagnostic models for non/mini-invasive assessment of DCM.

Rapid Screening Alpha-Glucosidase Inhibitors from Polygoni Vivipari Rhizoma by Multi-Step Matrix Solid-Phase Dispersion, Ultrafiltration and HPLC

Polygoni Vivipari Rhizoma (PVR), the dried root of Polygonum viviparum, has been used as herbal medicine in China for a long time. In the present study, a new method based on multi-step matrix solid-phase dispersion (MSPD), ultrafiltration and high performance liquid chromatography (HPLC) for screening alpha-glucosidase inhibitors (AGIs) from PVR was proposed. First, three different PVR extractions were prepared by multi-step MSPD with 15% methanol, 60% methanol and 100% methanol. Second, the alpha-glucosidase inhibition tests for the three extracts were carried out, and the 60% methanol extraction showed the best activity. Then, the AGIs screening experiment was performed with ultrafiltration and HPLC analysis using the 60% methanol extraction. Seven binding components (quercetin−3−O−vicianoside, quercetin 3−O−neohesperidoside, rutin, hyperoside, quercetin 3−O−glucuronide, luteolin−7−O−neohesperidoside, kaempferol 3−glucuronide) were found. These seven components were further validated as the AGIs by molecular docking analysis. The developed method was a rapid and efficient tool for screening AGIs from PVR, which provided scientific data for the bioactive components study of PVR.