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Background: Frailty and its components are proposed to associate with kidney function, but little attention is paid to the significance of changes in their status on rapid loss of kidney function. This study aimed to investigate the association between changes in frailty and its components status with rapid loss of renal function., Methods: This study used data from China Health and Retirement Longitudinal Study (CHARLS). Frailty status was measured using the Fried frailty phenotype (FP) scale, including five components: slowness, weakness, exhaustion, inactivity, and shrinking. Frailty status was further classified into three levels: robust (0 component), prefrail (1-2 components) and frail (3-5 components). Changes in frailty status were assessed by frailty status at baseline and 4- year follow-up. Rapid loss of kidney function was defined as a rate of estimate glomerular filtration rate(eGFR) decline ≥ 4 ml/min per 1.73 m 2 per year. Logistic regression models were performed to assess the association between changes in frailty status and its components status with rapid eGFR decline., Results: A total of 2705 participants were included with 316 (11.68%) participants categorized as rapid eGFR decline during the 4-year follow-up. Compared with baseline prefrail participants who progressed to frail, prefrail participants who maintained prefrail or recovered to robust status had decreased risks of rapid eGFR decline (stable prefrail status, OR = 0.608, 95% CI: 0.396-0.953; recover to robust, OR = 0.476, 95% CI: 0.266-0.846). In contrast, among baseline robust or frail participants, we did not find changes in frailty status significantly affect the risks of rapid loss of kidney function. Moreover, participants who experienced incident weakness showed the significant relationship with an increased risk of rapid eGFR decline (OR = 1.531, 95% CI: 1.051-2.198) compared to stable non-weakness participants. Other changes of frailty components status did not significantly affect the risks of rapid eGFR decline., Conclusions: The progression of frailty status increases the risks of rapid eGFR decline among prefrail populations. Preventing weakness, may benefit kidney function., (© 2024. The Author(s).)

Background: Accurate estimating glomerular filtration rate (GFR) is crucial both in clinical practice and epidemiological survey. We incorporated semi-supervised learning technology to improve GFR estimation performance., Methods: AASK [African American Study of Kidney Disease and Hypertension], CRIC [Chronic Renal Insufficiency Cohort] and DCCT [Diabetes Control and Complications Trial] studies were pooled together for model development, whereas MDRD [Modification of Diet in Renal Disease] and CRISP [Consortium for Radiological Imaging Studies of Polycystic Kidney Disease] studies for model external validation. A total of seven variables (Serum creatinine, Age, Sex, Black race, Diabetes status, Hypertension and Body Mass Index) were included as independent variables, while the outcome variable GFR was measured as the urinary clearance of 125 I-iothalamate. The revised CKD-EPI [Chronic Kidney Disease Epidemiology Collaboration] creatinine equations was selected as benchmark for performance comparisons. Head-to-head performance comparisons from four-variable to seven-variable combination were conducted between revised CKD-EPI equations and semi-supervised models., Results: In each independent variables combination, the semi-supervised models consistently achieved superior results in all three performance indicators compared with corresponding revised CKD-EPI equations in the external validation data set. Furthermore, compared with revised four-variable CKD-EPI equation, the seven-variable semi-supervised model performed less biased (mean of difference: 0.03 [- 0.28, 0.34] vs 1.53 [1.28, 1.85], P < 0.001), more precise (interquartile range of difference: 7.94 [7.37, 8.50] vs 8.28 [7.76, 8.83], P = 0.1) and accurate (P30: 88.9% [87.4%, 90.2%] vs 86.0% [84.4%, 87.4%], P < 0.001., Conclusions: The superior performance of the semi-supervised models during head-to-head comparisons supported the hypothesis that semi-supervised learning technology could improve GFR estimation performance., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: The recent guidelines recommend using the estimated glomerular filtration rate (eGFR) to evaluate renal function. There are two reported full-age-spectrum (FAS) equations in 2017, which are based on serum cystatin C concentrations with or without accompanying serum creatinine level (FAS Cr-Cys or FAS Cys ). We compared the performance and assessed the applicability of the new FAS equation with the 2012 CKD-EPI (CKD-EPI Cys and CKD-EPI Cr-Cys ) equation in Chinese subjects., Methods: A total of 1184 patients, mean aged 55.06 year who underwent 99m Tc-DTPA GFR measurements (rGFR) from four hospitals were enrolled. The bias (eGFR-rGFR), precision (interquartile range of difference [IQR]), and accuracy (the proportion of eGFR within 30% of rGFR [P30]) of eGFR and rGFR calculated by four equations were compared., Results: Generally, the equation based on the combination of Cys and Scr performed superior to that on the basis of Cys alone, either the CKD-EPI Cr-Cys or the FAS Cr-Cys . Detailedly, referred to rGFR (67.33 ml/min/1.73 m 2 ), the CKD-EPI Cys , CKD-EPI Cr-Cys , FAS Cys , and the FAS Cr-Cys estimated GFR 56.46 ml/min/1.73 m 2 , 62.79 ml/min/1.73 m 2 , 56.45 ml/min/1.73 m 2 , and 61.04 ml/min/1.73 m 2 , gave ROC AUC 0.944, 0.954, 0.943, and 0.953, respectively. Another comparison as to bias, precision, P 30 , and RMSE with FAS Cr-Cys were - 2.87 ml/min/1.73 m 2 , 19.01 ml/min/1.73 m 2 , 74.16%, and 17.84 ml/min/1.73 m 2 showed that FAS Cr-Cys performed approximately more accurate than other equations, as well as the diagnostic consistency of GFR staging. In the rGFR < 60 ml/min/1.73 m 2 subgroup, the FAS Cr-Cys equation showed the best performance. In older subjects, compared with FAS Cys , CKD-EPI Cr-Cys , and CKD-EPI Cys , the FAS Cr-Cys equation had relatively less bias (- 8.09 vs. - 9.63, - 7.52, - 11.04, P < 0.05), most precise (15.18 vs. 16.32, 15.22, 16.63), and most accuracy, P 30 was statistically different from the other equations, and achieved a ideal value > 70%., Conclusion: The performance of the FAS Cr-Cys equation is better than that of the CKD-EPI Cr-Cys equation in the Chinese population, particularly in the elderly. Yet, further modification of FAS equations from a large-scale study could be more suitable for the Chinese population, particularly in older people.

Background: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise., Methods: We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR., Results: Mean measured GFRs were 70.0 ml/min/1.73 m 2 in the developmental and 53.4 ml/min/1.73 m 2 in the external validation cohorts. In the external validation cohort, precision was better in the ensemble model of the ANN, SVM and regression equation (IQR = 13.5 ml/min/1.73 m 2 ) than in the new regression model (IQR = 14.0 ml/min/1.73 m 2 , P < 0.001). The precision of ensemble learning was the best of the three models, but the models had similar bias and accuracy. The median difference ranged from 2.3 to 3.7 ml/min/1.73 m 2 , 30% accuracy ranged from 73.1 to 76.0%, and P was > 0.05 for all comparisons of the new regression equation and the other new models., Conclusions: An ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates.

Background: Our previous studies demonstrated that 2012 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations were inaccurate enough for estimating glomerular filtration rate in elderly Chinese. Thus, this study was constructed to modify these equations., Methods: A total of 839 subjects (aged ≥60 years) from four hospitals in China between October 2009 and December 2014 were included. A random sample of 674 (80.3 %) subjects was used as a training sample set, and the remaining 165 (19.7 %) subjects as the verification set. The equations were modified using the hill-climbing algorithm., Results: Compared with the original CKD-EPI equations, the accuracy assessment indexes (bias, precision, accuracy and misclassification) were obviously improved after modification. Bland-Altman analysis also demonstrated that this modification attempt gained success to some extent., Conclusions: The modified 2012 CKD-EPI equations could be more accurate than the original equations for elderly Chinese individuals.

Background and Objectives: Serum β-trace protein (BTP) and β-2 microglobulin (B2M) are associated with risk of ESRD and death in the general population and in populations at high risk for these outcomes (GP/HR) and those with CKD, but results differ among studies., Design, Setting, Participants, & Measurements: We performed an individual patient-level meta-analysis including three GP/HR studies (n=17,903 participants) and three CKD studies (n=5415). We compared associations, risk prediction, and improvement in reclassification of eGFR using BTP (eGFR BTP ) and B2M (eGFR B2M ) alone and the average (eGFR avg ) of eGFR BTP , eGFR B2M , creatinine (eGFR cr ), and cystatin C (eGFR cys ), to eGFR cr , eGFR cys , and their combination (eGFR cr-cys ) for ESRD (2075 events) and death (7275 events)., Results: Mean (SD) follow up times for ESRD and mortality for GP/HR and CKD studies were 13 (4), 6.2 (3.2), 14 (5), and 7.5 (3.9) years, respectively. Compared with eGFR cr , eGFR BTP and eGFR B2M improved risk associations and modestly improved prediction for ESRD and death even after adjustment for established risk factors. eGFR avg provided the most consistent improvement in associations and prediction across both outcomes and populations. Assessment of heterogeneity did not yield clinically relevant differences. For ESRD, addition of albuminuria substantially attenuated the improvement in risk prediction and risk classification with novel filtration markers. For mortality, addition of albuminuria did not affect the improvement in risk prediction with the use of novel markers, but lessened improvement in risk classification, especially for the CKD cohort., Conclusions: These markers do not provide substantial additional prognostic information to eGFR cr and albuminuria, but may be appropriate in circumstances where eGFR cr is not accurate or albuminuria is not available. Educational efforts to increase measurement of albuminuria in clinical practice may be more cost-effective than measurement of BTP and B2M for improving prognostic information., (Copyright © 2016 by the American Society of Nephrology.)

Elevated homocysteine (HCY) and smoking are both important risk factors for hypertensive patients. However, whether they have crossing effect on renal function deterioration of hypertensive patients and what is the underlying mechanism are unclear. In the present study, 3033 participants diagnosed as essential hypertension with estimated glomerular filtration rate (eGFR)> 30 ml/min/1.73 m2 from southern China were enrolled in this cross-sectional study. We collected the demographic and clinical data. In addition, the mediation effects were analyzed. The results showed that, comparing with non-smokers, smokers had significant higher levels of HCY (13.10 (11.20-16.87) vs. 11.00 (8.90-13.40) umol/L, P < 0.001) and lower eGFR (79.71 (66.83-91.05) vs. 82.89 (69.80-95.85) ml/min/1.73m2, P < 0.001). HCY levels and smoking were independently associated with decreased eGFR. Meanwhile, eGFR levels were significantly negatively correlated with HCY (P < 0.001), and this correlation might be stronger in current smokers. Current smoker consuming over 20 cigarettes per day would accelerate early renal function deterioration (OR = 1.859, P = 0.019). The mediation effects analysis further showed that the association between smoking and renal function deterioration was mediated by HCY. And elevated HCY was accounted for 56.94% of the estimated causal effect of smoking on renal function deterioration in hypertensive patients. Our findings indicated that cigarette smoking was associated with renal function deterioration in hypertensive patients, and the association between cigarette smoking and renal function deterioration was probably mediated by elevated HCY. Therefore, HCY-lowering therapy may be beneficial for renal function deterioration in hypertensive smoking patients.

Introduction: Dyslipidemia is often detected in patients with chronic kidney disease (CKD). Previous studies of the relationship between lipid profiles and kidney function have yielded variable results. We aimed to investigate the correlation between serum lipid levels and kidney function evaluated by measured glomerular filtration rate (mGFR) in Chinese patients with CKD., Methods: A cross-sectional study was conducted on 2036 Chinese CKD patients who had mGFR. Linear regression analysis was performed to evaluate the correlation between different serum lipid levels and mGFR, while logistic regression analysis was used to investigate the association between CKD stages and the risk of different types of dyslipidemia., Results: The mean age was 55 years and the mean mGFR was 63 mL/min/1.73m2. After adjusting for some confounders (age, gender, body mass index, a history of diabetes, fasting glucose, a history of hypertension, systolic blood pressure, diastolic blood pressure, smoking status, hemoglobin, serum potassium, serum albumin, and serum uric acid), serum triglyceride level showed a negative correlation with mGFR (β = -0.006, P = 0.006) in linear regression analysis, and CKD stages were positively related to the risk of hypertriglyceridemia (odds ratios were 1.329, 1.868, 2.514 and P were 0.046, < 0.001, < 0.001 for CKD stage 2, 3, 4/5, respectively) in logistic regression anlysis., Conclusions: Serum triglyceride level is independently association with mGFR. Patients with reduced kidney function are more likely to have higher serum triglyceride levels. Further longitudinal, multicenter and well-conducted studies are needed to provide more evidence., Competing Interests: The authors have declared that no competing interests exist.

Aim: Various equations based on serum creatinine or/and cystatin C, required further validation in a Chinese population. We compared the performance of six Chinese equations (Mascr, Peiscr, Macys, Fengcys, Mascr-cys and Fengscr-cys) with the CKD-EPI equations in multi-centre Chinese subjects and evaluated their applicability in clinical practice., Methods: A total of 1522 adult patients from four different hospitals of China were enrolled in the study. (99m) Tc-DTPA renal dynamic imaging was used as the reference GFR (rGFR), and serum creatinine and cystatin C were measured by standardized assays. An optimal score system was implemented in the study., Results: The average rGFR of recruited subjects was 67.30±28.89 mL/min per 1.73m(2) . All estimated GFR (eGFR) correlated well with rGFR. In accordance with Bland-Altman analysis, the Fengscr-cys equations achieved optimal overall performance (score 14 vs 0-6), with least bias (median difference, -0.57 mL/min per 1.73m(2) ; median absolute difference, 8.83 mL/min per 1.73m(2) ), best precision (17.99 mL/min per 1.73m(2) ), highest accuracy (percentage of eGFR within 15%, 30% and 50% of the rGFR (P15 , P30 and P50 ; 49.7%, 78.7% and 91.8%, respectively); root-mean-square-error (RMSE, 16.28)). The Fengcys equation, a typical cystatin C based equation, was another well-behaved formula with an impressive performance. The Ma equations performed much poorer than the CKD-EPI equations. Consistent results can be observed in the GFR- /age- and sex-specific subgroups, while none equation yielded ideal accuracy in GFR<60 mL/min per 1.73 m(2) subgroup., Conclusion: The Fengscr-cys equation appeared to achieve the best performance for GFR estimation in overall Chinese adult patients. However, further research is warranted to improve the accuracy of available equations in GFR less than 60 mL/min per 1.73 m(2) individuals., (© 2015 Asian Pacific Society of Nephrology.)

Background: Previous researches has depicted that the performance of the recommended glomerular filtration rate (GFR)-estimating equations in the type 2 diabetic population is inferior to that in the non-diabetic population. We attempted to develop new GFR-predicting models for use in Chinese patients with type 2 diabetes in this study., Methods: We enrolled 519 type 2 diabetic patients including a development data-set (n = 276), an internal validation data-set (n = 138) and an external validation data-set (n = 105) to establish new GFR-predicting models. 99mTc-DTPA-GFR revised by the dual sample method was referred to as the gold GFR standard., Results: Based on sex, age, serum creatinine and new predictor variables [body mass index (BMI), hemoglobinA1C, and urinary albumin creatinine ratio], eight new regression models and eight artificial neural network (ANN) models were developed. In the external validation group, only ANN3 was superior in both precision and accuracy over the original CKD-EPI equation (precision, 20.5 vs. 24.2 mL/min/1.73 m(2), P < 0.001; 30 % accuracy, 88.6 vs. 80.6 %, P = 0.02)., Conclusions: ANN3 based on sex, age, serum creatinine and BMI is the optimal model for GFR estimation in Chinese patients with type 2 diabetes.

Background: Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates., Study Design: A study of diagnostic test accuracy., Setting & Participants: 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371)., Index Tests: Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation., Reference Test: Measured GFR (mGFR)., Other Measurements: GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry., Results: In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P<0.001 and P=0.02 compared to CKD-EPI and P<0.001 comparing the new regression and ANN models). Precisions (IQRs for the difference) were 22.6, 14.9, and 15.6 mL/min/1.73 m2, respectively (P<0.001 for both compared to CKD-EPI and P<0.001 comparing the new ANN and new regression models). Accuracies (proportions of eGFRs not deviating >30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (P<0.001 for both compared to CKD-EPI and P=0.5 comparing the new ANN and new regression models)., Limitations: Different methods for measuring GFR were a source of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution., Conclusions: An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation., (Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Background: We sought to develop a new equation to estimate glomerular filtration rate (GFR) in Chinese elderly population., Methods: A total of 668 Chinese elderly participants, including the development cohort (n = 433), the validation cohort (n = 235) were enrolled. The new equation using the generalized additive model, and age, gender, serum creatinine as predictor variables was developed and the performances was compared with the CKD-EPI equation., Results: In the validation data set, both bias and precision were improved with the new equation, as compared with the CKD-EPI equation (median difference, -1.5 ml/min/1.73 m(2) vs. 7.4 ml/min/1.73 m(2) for the new equation and the CKD-EPI equation, [P<0.001]; interquartile range [IQR] for the difference, 16.2 ml/min/1.73 m(2) vs. 19.0 ml/min/1.73 m(2) [P<0.001]), as were accuracies (15% accuracy, 40.4% vs. 30.6% [P = 0.02]; 30% accuracy, 71.1% vs. 47.2%, [P<0.001]; 50% accuracy, 90.2% vs. 75.7%, [P<0.001]), allowing improvement in GFR categorization (GFR category misclassification rate, 37.4% vs. 53.2% [P = <0.001])., Conclusions: A new equation was developed in Chinese elderly population. In the validation data set, the new equation performed better than the original CKD-EPI equation. The new equation needs further external validations. Calibration of the GFR referent standard to a more accurate one should be an useful way to improve the performance of GFR estimating equations.

Purpose: We sought to evaluate various glomerular filtration rate (GFR) estimating equations in elderly patients with chronic kidney disease (CKD)., Methods: A total of 332 Chinese elderly patients with CKD who had undergone technetium-99m diethylenetriaminepentaacetic acid ((99m)Tc-DTPA) renal dynamic imaging were enrolled. The MDRD equation, the re-expressed MDRD equation, the Chinese equation, the Ruijin equation, the previously Japanese equation, the new Japanese equation, the CKD-EPI equation, and the BIS1 equation were compared., Results: The median of difference between estimated GFR and standard GFR ranged from -9.78 to 1.79 ml/min/1.73 m(2). The interquartile range of difference ranged from 12.38 to 20.87 ml/min/1.73 m(2). Accuracy with a deviation <30 % ranged from 44.9 to 66.3 %. However, none of the equations had accuracy up to the 70 % level. Bland-Altman analysis demonstrated that the precision ranged from 50.5-87.6 ml/min/1.73 m(2). The slopes of regression line ranged from -0.26 to 0.55 and the intercepts ranged from -18.71 to 11.07. In both the overall performance and the performances in different stage of CKD, GFR estimated by the BIS1 equation performed better results., Conclusion: None of the GFR estimating equations are suitable for the specific Chinese population tested. At present, the BIS1 equation may be the optimal one for elderly Chinese CKD patients.

Background: Accurate and precise estimates of glomerular filtration rate (GFR) are essential for clinical assessments, and many methods of estimation are available. We developed a radial basis function (RBF) network and assessed the performance of this method in the estimation of the GFRs of 207 patients with type-2 diabetes and CKD., Methods: Standard GFR (sGFR) was determined by (99m)Tc-DTPA renal dynamic imaging and GFR was also estimated by the 6-variable MDRD equation and the 4-variable MDRD equation., Results: Bland-Altman analysis indicated that estimates from the RBF network were more precise than those from the other two methods for some groups of patients. However, the median difference of RBF network estimates from sGFR was greater than those from the other two estimates, indicating greater bias. For patients with stage I/II CKD, the median absolute difference of the RBF network estimate from sGFR was significantly lower, and the P50 of the RBF network estimate (n = 56, 87.5%) was significantly higher than that of the MDRD-4 estimate (n = 49, 76.6%) (p < 0.0167), indicating that the RBF network estimate provided greater accuracy for these patients., Conclusions: In patients with type-2 diabetes mellitus, estimation of GFR by our RBF network provided better precision and accuracy for some groups of patients than the estimation by the traditional MDRD equations. However, the RBF network estimates of GFR tended to have greater bias and higher than those indicated by sGFR determined by (99m)Tc-DTPA renal dynamic imaging.

Background: Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance., Methods: A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation., Results: In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m(2) vs. a range from 71.3 to 101.7 ml/min/1.73 m(2), allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network., Conclusions: A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.

Background: We aimed to evaluate the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine-cystatin C equation in a cohort of elderly Chinese participants., Materials and Methods: Glomerular filtration rate (GFR) was measured in 431 elderly Chinese participants by the technetium-99m diethylene-triamine-penta-acetic acid ((99m)Tc-DTPA) renal dynamic imaging method, and was calibrated equally to the dual plasma sample (99m)Tc-DTPA-GFR. Performance of the CKD-EPI creatinine-cystatin C equation was compared with the Cockcroft-Gault equation, the re-expressed 4-variable Modification of Diet in Renal Disease (MDRD) equation, and the CKD-EPI creatinine equation., Results: Although the bias of the CKD-EPI creatinine-cystatin C equation was greater than with the other equations (median difference, 5.7 mL/minute/1.73 m(2) versus a range from 0.4-2.5 mL/minute/1.73 m(2); P<0.001 for all), the precision was improved with the CKD-EPI creatinine-cystatin C equation (interquartile range for the difference, 19.5 mL/minute/1.73 m(2) versus a range from 23.0-23.6 mL/minute/1.73 m(2); P<0.001 for all comparisons), leading to slight improvement in accuracy (median absolute difference, 10.5 mL/minute/1.73 m(2) versus 12.2 and 11.4 mL/minute/1.73 m(2) for the Cockcroft-Gault equation and the re-expressed 4-variable MDRD equation, P=0.04 for both; 11.6 mL/minute/1.73 m(2) for the CKD-EPI creatinine equation, P=0.11), as the optimal scores of performance (6.0 versus a range from 1.0-2.0 for the other equations). Higher GFR category and diabetes were independent factors that negatively correlated with the accuracy of the CKD-EPI creatinine-cystatin C equation (β=-0.184 and -0.113, P<0.001 and P=0.02, respectively)., Conclusion: Compared with the creatinine-based equations, the CKD-EPI creatinine-cystatin C equation is more suitable for the elderly Chinese population. However, the cost-effectiveness of the CKD-EPI creatinine-cystatin C equation for clinical use should be considered.

Objectives: The aim of the present study was to evaluate modified glomerular filtration rate (GFR) prediction formulae in an elderly Chinese population with chronic kidney disease (CKD)., Methods: A total of 378 elderly Chinese patients with CKD were enrolled. The GFR was estimated with six modified GFR prediction formulae. The performances of the estimated GFRs were compared with those of the standard GFRs measured by technetium-99m diethyl-enetraminepentaacetic acid., Results: Biases were similar for Chinese formula 1, the Asian formula, and Chinese formula 2 (median difference, 2.22 mL/min/1.73 m(2) and 2.59 mL/min/1.73 m(2) for Chinese formula 1 and the Asian formula, respectively, versus (vs) 3.69 mL/min/1.73 m(2) for Chinese formula 2 [P = 0.298 and P = 0.913, respectively]). Precision was improved with the Japanese formula (interquartile range of the difference, 3.14 mL/min/1.73 m(2) of the Japanese formula versus 15.53-23.06 mL/min/1.73 m(2) of the other formulae). The accuracy of Chinese formula 2 was the highest (30% accuracy, 59.3% vs range 37.8-54.0% [P < 0.05 for all comparisons]). However, none of the modified formulae surpassed the acceptable tolerance (>70%), and the GFR category misclassification rates for all the formulae exceeded 50%., Conclusion: Our findings suggest that all six modified formulae developed in Asian populations may show great bias in elderly Chinese patients with CKD. Also, our study suggests the need for uniform measures for the assessment of CKD in the elderly to guarantee better sensitivity and specificity.

Background: Chronic kidney disease (CKD) is recognized worldwide as a public health problem, and its prevalence increases as the population ages. However, the applicability of formulas for estimating the glomerular filtration rate (GFR) based on serum creatinine (SC) levels in elderly Chinese patients with CKD is limited., Materials and Methods: Based on values obtained with the technetium-99m diethylenetriaminepentaacetic acid ((99m)Tc-DTPA) renal dynamic imaging method, 319 elderly Chinese patients with CKD were enrolled in this study. Serum creatinine was determined by the enzymatic method. The GFR was estimated using the Cockroft-Gault (CG) equation, the Modification of Diet in Renal Disease (MDRD) equations, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, the Jelliffe-1973 equation, and the Hull equation., Results: The median of difference ranged from -0.3-4.3 mL/min/1.73 m(2). The interquartile range (IQR) of differences ranged from 13.9-17.6 mL/min/1.73 m(2). Accuracy with a deviation less than 15% ranged from 27.6%-32.9%. Accuracy with a deviation less than 30% ranged from 53.6%-57.7%. Accuracy with a deviation less than 50% ranged from 74.9%-81.5%. None of the equations had accuracy up to the 70% level with a deviation less than 30% from the standard glomerular filtration rate (sGFR). Bland-Altman analysis demonstrated that the mean difference ranged from -3.0-2.4 mL/min/1.73 m(2). However, the agreement limits of all the equations, except the CG equation, exceeded the prior acceptable tolerances defined as 60 mL/min/1.73 m(2). When the overall performance and accuracy were compared in different stages of CKD, GFR estimated using the CG equation showed promising results., Conclusions: Our study indicated that none of these equations were suitable for estimating GFR in the elderly Chinese population investigated. At present, based on overall performance, as well as performance in different CKD stages, the CG equation may be the most accurate for estimating GFR in elderly Chinese patients with CKD.

Purpose: Scant research has been conducted on the interplay between the systemic inflammation response index (SIRI) and chronic kidney disease (CKD). The present study endeavors to meticulously scrutinize the association between SIRI and renal function. Additionally, we aim to assess its efficacy in predicting the progression of CKD in non-dialysis patients.Patients and Methods: Adult patients with CKD who were not undergoing dialysis were enrolled, and follow-up data were obtained. Data from distinct groups were extracted and meticulously compared. A comprehensive analytical approach was adopted, including logistic regression analysis, Kaplan-Meier analysis, Cox proportional hazards regression analysis, and subgroup analysis.Results: Our study included 1420 patients, with a mean age of 61 ± 17 years, and 63% were male. 244 (17.2%) patients experienced the progression of CKD. As the level of ln(SIRI) increased, patients tended to be older, with a higher proportion of males, and increased prevalence rates of hypertension, stroke, heart failure, and progression of CKD. Additionally, the levels of baseline creatinine and C-reactive protein were elevated, while the levels of estimated glomerular filtration rate and hemoglobin decreased. Upon adjusting for demographic and biochemical variables, logistic regression analysis indicated that ln(SIRI) was independently associated with advanced CKD in pre-dialysis patients (OR=1.59, 95% CI: 1.29– 1.95, P< 0.001). Moreover, Cox proportional-hazard analysis revealed that ln(SIRI) independently predicted CKD progression (HR: 1.3, 95% CI: 1.07– 1.59, P=0.009). Conducting a subgroup analysis, we observed significant interactions between ln(SIRI) levels and gender (p< 0.001), age (p=0.046), and hypertension (p=0.028) in relation to the progression of CKD.Conclusion: Our study's findings demonstrate a significant association between SIRI and fundamental renal function, and independently establish a correlation between SIRI and the progression of CKD in pre-dialysis patients. These observations suggest that SIRI holds promise as a potential predictor for CKD progression. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. Methods: An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient's measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. Results: A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. Conclusions: This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function. [ABSTRACT FROM AUTHOR]

Introduction: The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation. Methods: Fifty-three CKD participants were recruited from the Third Affiliated Hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst 5.0 for one-way analysis of variance, principal component analysis, and partial least squares-discriminant analysis and confirmed the metabolites whose levels increased or decreased with mGFR and variable importance in projection (VIP) values >1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient >0.8 and VIP >2 were identified. Results: We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites. Conclusion: We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function.Methods: Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory.Results: From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study.Conclusions: Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation. [ABSTRACT FROM AUTHOR]

Background: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise.Methods: We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR.Results: Mean measured GFRs were 70.0 ml/min/1.73 m2 in the developmental and 53.4 ml/min/1.73 m2 in the external validation cohorts. In the external validation cohort, precision was better in the ensemble model of the ANN, SVM and regression equation (IQR = 13.5 ml/min/1.73 m2) than in the new regression model (IQR = 14.0 ml/min/1.73 m2, P < 0.001). The precision of ensemble learning was the best of the three models, but the models had similar bias and accuracy. The median difference ranged from 2.3 to 3.7 ml/min/1.73 m2, 30% accuracy ranged from 73.1 to 76.0%, and P was > 0.05 for all comparisons of the new regression equation and the other new models.Conclusions: An ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates. [ABSTRACT FROM AUTHOR]

Background: Previous researches has depicted that the performance of the recommended glomerular filtration rate (GFR)-estimating equations in the type 2 diabetic population is inferior to that in the non-diabetic population. We attempted to develop new GFR-predicting models for use in Chinese patients with type 2 diabetes in this study.Methods: We enrolled 519 type 2 diabetic patients including a development data-set (n = 276), an internal validation data-set (n = 138) and an external validation data-set (n = 105) to establish new GFR-predicting models. 99mTc-DTPA-GFR revised by the dual sample method was referred to as the gold GFR standard.Results: Based on sex, age, serum creatinine and new predictor variables [body mass index (BMI), hemoglobinA1C, and urinary albumin creatinine ratio], eight new regression models and eight artificial neural network (ANN) models were developed. In the external validation group, only ANN3 was superior in both precision and accuracy over the original CKD-EPI equation (precision, 20.5 vs. 24.2 mL/min/1.73 m(2), P < 0.001; 30 % accuracy, 88.6 vs. 80.6 %, P = 0.02).Conclusions: ANN3 based on sex, age, serum creatinine and BMI is the optimal model for GFR estimation in Chinese patients with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Objective: To improve the performance of glomerular filtration rate (GFR) estimating equation in Chinese type 2 diabetic patients by modification of the CKD-EPI equation. Design and patients: A total of 1196 subjects were enrolled. Measured GFR was calibrated to the dual plasma sample 99mTc-DTPA-GFR. GFRs estimated by the re-expressed 4-variable MDRD equation, the CKD-EPI equation and the Asian modified CKD-EPI equation were compared in 351 diabetic/non-diabetic pairs. And a new modified CKD-EPI equation was reconstructed in a total of 589 type 2 diabetic patients. Results: In terms of both precision and accuracy, GFR estimating equations all achieved better results in the non-diabetic cohort comparing with those in the type 2 diabetic cohort (30% accuracy, P≤0.01 for all comparisons). In the validation data set, the new modified equation showed less bias (median difference, 2.3 ml/min/1.73 m2 for the new modified equation vs. ranged from −3.8 to −7.9 ml/min/1.73 m2 for the other 3 equations [P<0.001 for all comparisons]), as was precision (IQR of the difference, 24.5 ml/min/1.73 m2 vs. ranged from 27.3 to 30.7 ml/min/1.73 m2), leading to a greater accuracy (30% accuracy, 71.4% vs. 55.2% for the re-expressed 4 variable MDRD equation and 61.0% for the Asian modified CKD-EPI equation [P = 0.001 and P = 0.02]). Conclusion: A new modified CKD-EPI equation for type 2 diabetic patients was developed and validated. The new modified equation improves the performance of GFR estimation. [ABSTRACT FROM AUTHOR]

Background: Sirtuin 3 (Sirt3) is a key regulator of energy metabolism and oxidative stress. To investigate the role of Sirt3 in contrast-induced acute kidney injury (CIAKI), we established the model both in vivo and in vitro to explore the potential mechanisms.Methods: In vivo, we established CIAKI models in wild-type (WT) and Sirt3-knockout (Sirt3-KO) mice. Blood urea nitrogen (BUN) and serum creatinine (Scr) were detected by enzyme-linked immunosorbent assay, Glomerular Filtration Rate (GFR) and creatinine clearance were also investigated. We detected the production of reactive oxygen species (ROS) via 2'7'-dichlorodihydro-fluorescein diacetate. The expressions of Sirt3, oxidative stress and apoptosis related markers (MnSOD, Catalase, Acetyl-MnSOD K68, Nox4, Bax, Bcl-2 and Caspase3) were measured and analyzed. In addition, we observed the effect of nicotinamide riboside (NR) on CIAKI in WT and Sirt3-KO mice. In vitro, Sirt3 was knocked out by siRNA transfection method in HK-2 cells. Sirt3, ROS, oxidative stress and apoptosis markers in HK-2 cells were also measured.Results: Our data demonstrated that the levels of Scr and BUN in Sirt3-KO mice were increased while the levels of the GFR and creatinine clearance were decreased in CIAKI mice. In Sirt3-KO or siRNA groups, the activities of MnSOD and Catalase were markedly down-regulated. Also, the expression of Caspase3 were markedly increased and the ratio of Bcl-2/Bax was decreased, while the ROS level was increased in Sirt3 deficiency groups. NR ameliorated CIAKI in WT mice but not in Sirt3-KO mice.Conclusion: Our results suggest that Sirt3 deficiency aggravates contrast-induced acute kidney injury. Sirt3 is critical in NR-mediated renoprotection in CIAKI. [ABSTRACT FROM AUTHOR]