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The purpose of this study was to perform a comparative biochemical analysis between conventional spectrophotometry and Raman spectroscopy, techniques used for diagnoses, on the urine of healthy (CT) and diabetic and hypertensive patients (DM&HBP). Urine from 40 subjects (20 in the CT group and 20 in the DM&HBP group) was examined in a dispersive Raman spectrometer (an 830 nm excitation and a 350 mW power). The mean Raman spectra between both groups showed a significant difference in peaks of glucose; exploratory analysis by principal component analysis (PCA) identified spectral differences between the groups, with higher peaks of glucose and proteins in the DM&HBP group. A partial least squares (PLS) regression model estimated by the Raman data indicated the concentrations of urea, creatinine, glucose, phosphate, and total protein; creatinine and glucose were the biomarkers that presented the best correlation coefficient ( r ) between the two techniques analyzed ( r = 0.68 and r = 0.98, respectively), both with eight latent variables (LVs) and a root mean square error of cross-validation (RMSecv) of 3.6 and 5.1 mmol/L (41 and 92 mg/dL), respectively. Discriminant analysis (PLS-DA) using the entire Raman spectra was able to differentiate the samples of the groups in the study, with a higher accuracy (81.5%) compared to the linear discriminant analysis (LDA) models using the concentration values of the spectrometric analysis (60.0%) and the concentrations predicted by the PLS regression (69.8%). Results indicated that spectral models based on PLS applied to Raman spectra may be used to distinguish subjects with diabetes and blood hypertension from healthy ones in urinalysis aimed at population screening.

Patients with diabetes mellitus and hypertension (HT) diseases are predisposed to kidney diseases. The objective of this study was to identify potential biomarkers in the urine of diabetic and hypertensive patients through Raman spectroscopy in order to predict the evolution to complications and kidney failure. Urine samples were collected from control subjects (CTR) and patients with diabetes and HT with no complications (lower risk, LR), high degree of complications (higher risk, HR), and doing blood dialysis (DI). Urine samples were stored frozen (-20°C) before spectral analysis. Raman spectra were obtained using a dispersive spectrometer (830-nm, 300-mW power, and 20-s accumulation). Spectra were then submitted to principal component analysis (PCA) followed by discriminant analysis. The first PCA loading vectors revealed spectral features of urea, creatinine, and glucose. It has been found that the amounts of urea and creatinine decreased as disease evoluted from CTR to LR/HR and DI (PC1, p<0.05), and the amount of glucose increased in the urine of LR/HR compared to CTR (PC3, p<0.05). The discriminating model showed better overall classification rate of 70%. These results could lead to diagnostic information of possible complications and a better disease prognosis.