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Background: Renal denervation (RDN) has emerged in recent years as a possible treatment for hypertension. The first sham-controlled trial showed a small magnitude and non-significant in the blood pressure (BP) lowering effect, also due to a substantial decrease of BP in sham arm. Considering this, we aimed to quantify the magnitude of BP decrease within the sham arm of Randomized Controlled Trials (RCT) with RDN in patients with hypertension., Methods: Electronic databases were searched since inception until January 2022 for randomized sham-controlled trials which assessed the efficacy in lowering BP of the sham intervention for catheter-based RDN in adult patients with hypertension. The outcomes were change in ambulatory/office systolic and diastolic BP., Results: A total of 9 RCT were included in the analysis enrolling a total of 674 patients. Sham intervention showed a decrease in all evaluated outcomes. Office systolic BP had a reduction of -5.52 mmHg [95%CI -7.91, -3.13] and office diastolic BP of -2.13 mmHg [95%CI -3.08, -1.17]. Sham procedure for RDN also showed a reduction of -3.41 mmHg [95%CI -5.08, -1.75] in ambulatory systolic BP and - 2.44 mmHg [95%CI -3.31, -1.57] in ambulatory diastolic BP., Conclusion: Despite recent data indicating that RDN might be an effective treatment for patients with resistant hypertension when compared to a sham intervention, our results indicate that the sham intervention for RDN also has a significant effect on lowering Office and Ambulatory (24-h) Blood Pressure in adult patients with hypertension. This highlights that BP itself might be sensitive to placebo-like effect and also brings further difficulties in establishing the BP lowering efficacy of invasive interventions due to the magnitude of the sham effect., (© 2023. The Author(s).)

Higher blood pressure level and poor glycemic control in diabetic patients are considered progression factors that cause faster decline in kidney functions leading to kidney damage. The present study aimed to develop a quantification model of biomarkers creatinine, urea, and glucose by means of selected peaks of these compounds, measured by Raman spectroscopy, and to estimate the concentration of these analytes in the urine of normal subjects (G&#95;N), diabetic patients with hypertension (G&#95;WOL) patients with chronic renal failure doing dialysis (G&#95;D). Raman peak intensities at 680 cm -1 (creatinine), 1004 cm -1 (urea), and 1128 cm -1 (glucose) from normal, diabetic, and hypertensive and doing dialysis patients, obtained with a dispersive 830 nm Raman spectrometer, were estimated through Origin software. Spectra of creatinine, urea, and glucose diluted in water were also obtained, and the same peaks were evaluated. A discrimination model based on Mahalanobis distance was developed. It was possible to determine the concentration of creatinine, urea, and glucose by means of the Raman peaks of the selected biomarkers in the urine of the groups G&#95;N, G&#95;WOL, and G&#95;D (r = 0.9). It was shown that the groups G&#95;WOL and G&#95;D had lower creatinine and urea concentrations than the group G&#95;N (p < 0.05). The classification model based on Mahalanobis distance applied to the concentrations of creatinine, urea, and glucose presented a correct classification of 89% for G&#95;N, 86% for G&#95;WOL, and 79% for G&#95;D. It was possible to obtain quantitative information regarding important biomarkers in urine for the assessment of renal impairment in patients with diabetes and hypertension, and this information can be correlated with clinical criteria for the diagnosis of chronic kidney disease.

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.

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. [ABSTRACT FROM AUTHOR]

Higher blood pressure level and poor glycemic control in diabetic patients are considered progression factors that cause faster decline in kidney functions leading to kidney damage. The present study aimed to develop a quantification model of biomarkers creatinine, urea, and glucose by means of selected peaks of these compounds, measured by Raman spectroscopy, and to estimate the concentration of these analytes in the urine of normal subjects (G_N), diabetic patients with hypertension (G_WOL) patients with chronic renal failure doing dialysis (G_D). Raman peak intensities at 680 cm-1 (creatinine), 1004 cm-1 (urea), and 1128 cm-1 (glucose) from normal, diabetic, and hypertensive and doing dialysis patients, obtained with a dispersive 830 nm Raman spectrometer, were estimated through Origin software. Spectra of creatinine, urea, and glucose diluted in water were also obtained, and the same peaks were evaluated. A discrimination model based on Mahalanobis distance was developed. It was possible to determine the concentration of creatinine, urea, and glucose by means of the Raman peaks of the selected biomarkers in the urine of the groups G_N, G_WOL, and G_D (r = 0.9). It was shown that the groups G_WOL and G_D had lower creatinine and urea concentrations than the group G_N (p < 0.05). The classification model based on Mahalanobis distance applied to the concentrations of creatinine, urea, and glucose presented a correct classification of 89% for G_N, 86% for G_WOL, and 79% for G_D. It was possible to obtain quantitative information regarding important biomarkers in urine for the assessment of renal impairment in patients with diabetes and hypertension, and this information can be correlated with clinical criteria for the diagnosis of chronic kidney disease. [ABSTRACT FROM AUTHOR]