Plasma Uromodulin Correlates With Kidney Function and Identifies Early Stages in Chronic Kidney Disease Patients

Uromodulin, released from tubular cells of the ascending limb into the blood, may be associated with kidney function. This work studies the relevance of plasma uromodulin as a biomarker for kidney function in an observational cohort of chronic kidney disease (CKD) patients and subjects without CKD (CKD stage 0). It should be further evaluated if uromodulin allows the identification of early CKD stages. Plasma uromodulin, serumcreatinine, cystatin C, blood-urea-nitrogen (BUN) concentrations, and estimated glomerular filtration rate (eGFR CKD-EPIcrea-cystatin) were assessed in 426 individuals of whom 71 were CKD stage 0 and 355 had CKD. Besides descriptive statistics, univariate correlations between uromodulin and biomarkers/eGFR were calculated using Pearson-correlation coefficient. Multiple linear regression modeling was applied to establish the association between uromodulin and eGFR adjusted for demographic parameters and pharmacologic treatment. Receiver-operating-characteristic (ROC) analysis adjusted for demographic parameters was performed to test if uromodulin allows differentiation of subjects with CKD stage 0 and CKD stage I. Mean uromodulin plasma levels were 85.7 +/- 60.5 ng/mL for all CKD stages combined. Uromodulin was correlated with all biomarkers/eGFR in univariate analysis (eGFR: r = 0.80, creatinine: r = +/- 0.76, BUN: r = +/- 0.72, and cystatin C: r = +/- 0.79). Multiple linear regression modeling showed significant association between uromodulin and eGFR (coefficient estimate beta = 0.696, 95% confidence interval [CI] 0.603-0.719, P<0.001). In ROC analysis uromodulin was the only parameter that significantly improved a model containing demographic parameters to differentiate between CKD 0 degrees and I degrees (area under the curve [AUC] 0.831, 95% CI 0.746-0.915, P = 0.008) compared to creatinine, cystatin C, BUN, and eGFR (AUC for creatinine: 0.722, P = 0.056, cystatin C: 0.668, P = 0.418, BUN: 0.653, P = 0.811, and eGFR: 0.634, P = 0.823). Plasma uromodulin serves as a robust biomarker for kidney function and uniquely allows the identification of early stages of CKD. As a marker of tubular secretion it might represent remaining nephron mass and therefore intrinsic "kidney function'' rather than just glomerular filtration, the latter only being of limited value to represent kidney function as a whole. It therefore gives substantial information on the renal situation in addition to glomerular filtration and potentially solves the problem of creatinine-blind range of CKD, in which kidney impairment often remains undetected

Plasma Uromodulin Correlates With Kidney Function and Identifies Early Stages in Chronic Kidney Disease Patients

Uromodulin, released from tubular cells of the ascending limb into the blood, may be associated with kidney function. This work studies the relevance of plasma uromodulin as a biomarker for kidney function in an observational cohort of chronic kidney disease (CKD) patients and subjects without CKD (CKD stage 0). It should be further evaluated if uromodulin allows the identification of early CKD stages. Plasma uromodulin, serumcreatinine, cystatin C, blood-urea-nitrogen (BUN) concentrations, and estimated glomerular filtration rate (eGFR CKD-EPIcrea-cystatin) were assessed in 426 individuals of whom 71 were CKD stage 0 and 355 had CKD. Besides descriptive statistics, univariate correlations between uromodulin and biomarkers/eGFR were calculated using Pearson-correlation coefficient. Multiple linear regression modeling was applied to establish the association between uromodulin and eGFR adjusted for demographic parameters and pharmacologic treatment. Receiver-operating-characteristic (ROC) analysis adjusted for demographic parameters was performed to test if uromodulin allows differentiation of subjects with CKD stage 0 and CKD stage I. Mean uromodulin plasma levels were 85.7 +/- 60.5 ng/mL for all CKD stages combined. Uromodulin was correlated with all biomarkers/eGFR in univariate analysis (eGFR: r = 0.80, creatinine: r = +/- 0.76, BUN: r = +/- 0.72, and cystatin C: r = +/- 0.79). Multiple linear regression modeling showed significant association between uromodulin and eGFR (coefficient estimate beta = 0.696, 95% confidence interval [CI] 0.603-0.719, P<0.001). In ROC analysis uromodulin was the only parameter that significantly improved a model containing demographic parameters to differentiate between CKD 0 degrees and I degrees (area under the curve [AUC] 0.831, 95% CI 0.746-0.915, P = 0.008) compared to creatinine, cystatin C, BUN, and eGFR (AUC for creatinine: 0.722, P = 0.056, cystatin C: 0.668, P = 0.418, BUN: 0.653, P = 0.811, and eGFR: 0.634, P = 0.823). Plasma uromodulin serves as a robust biomarker for kidney function and uniquely allows the identification of early stages of CKD. As a marker of tubular secretion it might represent remaining nephron mass and therefore intrinsic "kidney function'' rather than just glomerular filtration, the latter only being of limited value to represent kidney function as a whole. It therefore gives substantial information on the renal situation in addition to glomerular filtration and potentially solves the problem of creatinine-blind range of CKD, in which kidney impairment often remains undetected

Cardiovascular Mortality Can Be Predicted by Heart Rate Turbulence in Hemodialysis Patients

Background: Excess mortality in hemodialysis patients is mostly of cardiovascular origin. We examined the association of heart rate turbulence (HRT), a marker of baroreflex sensitivity, with cardiovascular mortality in hemodialysis patients. Methods: A population of 290 prevalent hemodialysis patients was followed up for a median of 3 years. HRT categories 0 (both turbulence onset [TO] and slope [TS] normal), 1 (TO or TS abnormal), and 2 (both TO and TS abnormal) were obtained from 24 h Holter recordings. The primary end-point was cardiovascular mortality. Associations of HRT categories with the endpoints were analyzed by multivariable Cox regression models including HRT, age, albumin, and the improved Charlson Comorbidity Index for hemodialysis patients. Multivariable linear regression analysis identified factors associated with TO and TS. Results: During the follow-up period, 20 patients died from cardiovascular causes. In patients with HRT categories 0, 1 and 2, cardiovascular mortality was 1, 10, and 22%, respectively. HRT category 2 showed the strongest independent association with cardiovascular mortality with a hazard ratio of 19.3 (95% confidence interval: 3.69-92.03;P < 0.001). Age, calcium phosphate product, and smoking status were associated with TO and TS. Diabetes mellitus and diastolic blood pressure were only associated with TS. Conclusion: Independent of known risk factors, HRT assessment allows identification of hemodialysis patients with low, intermediate, and high risk of cardiovascular mortality. Future prospective studies are needed to translate risk prediction into risk reduction in hemodialysis patients

Rationale and study design of the prospective, longitudinal, observational cohort study “rISk strAtification in end-stage renal disease” (ISAR) study

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro-and macrocirculation and to determine autonomic function. Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality. Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study

Rationale and study design of the prospective, longitudinal, observational cohort study "rISk strAtification in end-stage renal disease" (ISAR) study

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro-and macrocirculation and to determine autonomic function. Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality. Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study

Plasma Uromodulin Correlates With Kidney Function and Identifies Early Stages in Chronic Kidney Disease Patients

Uromodulin, released from tubular cells of the ascending limb into the blood, may be associated with kidney function. This work studies the relevance of plasma uromodulin as a biomarker for kidney function in an observational cohort of chronic kidney disease (CKD) patients and subjects without CKD (CKD stage 0). It should be further evaluated if uromodulin allows the identification of early CKD stages. Plasma uromodulin, serumcreatinine, cystatin C, blood-urea-nitrogen (BUN) concentrations, and estimated glomerular filtration rate (eGFR CKD-EPIcrea-cystatin) were assessed in 426 individuals of whom 71 were CKD stage 0 and 355 had CKD. Besides descriptive statistics, univariate correlations between uromodulin and biomarkers/eGFR were calculated using Pearson-correlation coefficient. Multiple linear regression modeling was applied to establish the association between uromodulin and eGFR adjusted for demographic parameters and pharmacologic treatment. Receiver-operating-characteristic (ROC) analysis adjusted for demographic parameters was performed to test if uromodulin allows differentiation of subjects with CKD stage 0 and CKD stage I. Mean uromodulin plasma levels were 85.7 +/- 60.5 ng/mL for all CKD stages combined. Uromodulin was correlated with all biomarkers/eGFR in univariate analysis (eGFR: r = 0.80, creatinine: r = +/- 0.76, BUN: r = +/- 0.72, and cystatin C: r = +/- 0.79). Multiple linear regression modeling showed significant association between uromodulin and eGFR (coefficient estimate beta = 0.696, 95% confidence interval [CI] 0.603-0.719, P<0.001). In ROC analysis uromodulin was the only parameter that significantly improved a model containing demographic parameters to differentiate between CKD 0 degrees and I degrees (area under the curve [AUC] 0.831, 95% CI 0.746-0.915, P = 0.008) compared to creatinine, cystatin C, BUN, and eGFR (AUC for creatinine: 0.722, P = 0.056, cystatin C: 0.668, P = 0.418, BUN: 0.653, P = 0.811, and eGFR: 0.634, P = 0.823). Plasma uromodulin serves as a robust biomarker for kidney function and uniquely allows the identification of early stages of CKD. As a marker of tubular secretion it might represent remaining nephron mass and therefore intrinsic "kidney function'' rather than just glomerular filtration, the latter only being of limited value to represent kidney function as a whole. It therefore gives substantial information on the renal situation in addition to glomerular filtration and potentially solves the problem of creatinine-blind range of CKD, in which kidney impairment often remains undetected

Challenging Recently Published Parameter Sets for Entropy Measures in Risk Prediction for End-Stage Renal Disease Patients

Heart rate variability (HRV) analysis is a non-invasive tool for assessing cardiac health. Entropy measures quantify the chaotic properties of HRV, but they are sensitive to the choice of their required parameters. Previous studies therefore have performed parameter optimization, targeting solely their particular patient cohort. In contrast, this work aimed to challenge entropy measures with recently published parameter sets, without time-consuming optimization, for risk prediction in end-stage renal disease patients. Approximate entropy, sample entropy, fuzzy entropy, fuzzy measure entropy, and corrected approximate entropy were examined. In total, 265 hemodialysis patients from the ISAR (rISk strAtification in end-stage Renal disease) study were analyzed. Throughout a median follow-up time of 43 months, 70 patients died. Fuzzy entropy and corrected approximate entropy (CApEn) provided significant hazard ratios, which remained significant after adjustment for clinical risk factors from literature if an entropy maximizing threshold parameter was chosen. Revealing results were seen in the subgroup of patients with heart disease (HD) when setting the radius to a multiple of the data’s standard deviation ( r = 0.2 · σ ); all entropies, except CApEn, predicted mortality significantly and remained significant after adjustment. Therefore, these two parameter settings seem to reflect different cardiac properties. This work shows the potential of entropy measures for cardiovascular risk stratification in cohorts the parameters were not optimized for, and it provides additional insights into the parameter choice