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      Targeting Uric Acid and the Inhibition of Progression to End-Stage Renal Disease—A Propensity Score Analysis

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          Abstract

          Background

          The role of uric acid (UA) in the progression of chronic kidney disease (CKD) remains controversial due to the unavoidable cause and result relationship. This study was aimed to clarify the independent impact of UA on the subsequent risk of end-stage renal disease (ESRD) by a propensity score analysis.

          Methods

          A retrospective CKD cohort was used (n = 803). Baseline 23 covariates were subjected to a multivariate binary logistic regression with the targeted time-averaged UA of 6.0, 6.5 or 7.0 mg/dL. The participants trimmed 2.5 percentile from the extreme ends of the cohort underwent propensity score analyses consisting of matching, stratification on quintile and covariate adjustment. Covariate balances after 1:1 matching without replacement were tested for by paired analysis and standardized differences. A stratified Cox regression and a Cox regression adjusted for logit of propensity scores were examined.

          Results

          After propensity score matching, the higher UA showed elevated hazard ratios (HRs) by Kaplan-Meier analysis (≥6.0 mg/dL, HR 4.53, 95%CI 1.79–11.43; ≥6.5 mg/dL, HR 3.39, 95%CI 1.55–7.42; ≥7.0 mg/dL, HR 2.19, 95%CI 1.28–3.75). The number needed to treat was 8 to 9 over 5 years. A stratified Cox regression likewise showed significant crude HRs (≥6.0 mg/dL, HR 3.63, 95%CI 1.25–10.58; ≥6.5 mg/dL, HR 3.46, 95%CI 1.56–7.68; ≥7.0 mg/dL, HR 2.05, 95%CI 1.21–3.48). Adjusted HR lost its significance at 6.0 mg/dL. The adjustment for the logit of the propensity scores showed the similar results but with worse model fittings than the stratification method. Upon further adjustment for other covariates the significance was attained at 6.5 mg/dL.

          Conclusions

          Three different methods of the propensity score analysis showed consistent results that the higher UA accelerates the progression to the subsequent ESRD. A stratified Cox regression outperforms other methods in generalizability and adjusting for residual bias. Serum UA should be targeted less than 6.5 mg/dL.

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          Most cited references26

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          A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.

          Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matching on the propensity score eliminates a greater proportion of baseline differences between treated and untreated subjects than does stratification on the propensity score. However, the analysis of propensity-score-matched samples requires statistical methods appropriate for matched-pairs data. We critically evaluated 47 articles that were published between 1996 and 2003 in the medical literature and that employed propensity-score matching. We found that only two of the articles reported the balance of baseline characteristics between treated and untreated subjects in the matched sample and used correct statistical methods to assess the degree of imbalance. Thirteen (28 per cent) of the articles explicitly used statistical methods appropriate for the analysis of matched data when estimating the treatment effect and its statistical significance. Common errors included using the log-rank test to compare Kaplan-Meier survival curves in the matched sample, using Cox regression, logistic regression, chi-squared tests, t-tests, and Wilcoxon rank sum tests in the matched sample, thereby failing to account for the matched nature of the data. We provide guidelines for the analysis and reporting of studies that employ propensity-score matching. Copyright (c) 2007 John Wiley & Sons, Ltd.
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            Effect of allopurinol in chronic kidney disease progression and cardiovascular risk.

            Hyperuricemia is associated with hypertension, inflammation, renal disease progression, and cardiovascular disease. However, no data are available regarding the effect of allopurinol in patients with chronic kidney disease. We conducted a prospective, randomized trial of 113 patients with estimated GFR (eGFR) <60 ml/min. Patients were randomly assigned to treatment with allopurinol 100 mg/d (n = 57) or to continue the usual therapy (n = 56). Clinical, biochemical, and inflammatory parameters were measured at baseline and at 6, 12, and 24 months of treatment. The objectives of study were: (1) renal disease progression; (2) cardiovascular events; and (3) hospitalizations of any causes. Serum uric acid and C-reactive protein levels were significantly decreased in subjects treated with allopurinol. In the control group, eGFR decreased 3.3 +/- 1.2 ml/min per 1.73 m(2), and in the allopurinol group, eGFR increased 1.3 +/- 1.3 ml/min per 1.73 m(2) after 24 months. Allopurinol treatment slowed down renal disease progression independently of age, gender, diabetes, C-reactive protein, albuminuria, and renin-angiotensin system blockers use. After a mean follow-up time of 23.4 +/- 7.8 months, 22 patients suffered a cardiovascular event. Diabetes mellitus, previous coronary heart disease, and C-reactive protein levels increased cardiovascular risk. Allopurinol treatment reduces risk of cardiovascular events in 71% compared with standard therapy. Allopurinol decreases C-reactive protein and slows down the progression of renal disease in patients with chronic kidney disease. In addition, allopurinol reduces cardiovascular and hospitalization risk in these subjects.
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              Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches.

              A central problem in public health studies is how to make inferences about the causal effects of treatments or agents. In this article we review an approach to making such inferences via potential outcomes. In this approach, the causal effect is defined as a comparison of results from two or more alternative treatments, with only one of the results actually observed. We discuss the application of this approach to a number of data collection designs and associated problems commonly encountered in clinical research and epidemiology. Topics considered include the fundamental role of the assignment mechanism, in particular the importance of randomization as an unconfounded method of assignment; randomization-based and model-based methods of statistical inference for causal effects; methods for handling noncompliance and missing data; and methods for limiting bias in the analysis of observational data, including propensity score matching and sensitivity analysis.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 December 2015
                2015
                : 10
                : 12
                : e0145506
                Affiliations
                [1 ]Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan
                [2 ]Department of Nephrology, Tianjin First Central Hospital, Tianjin, China
                [3 ]Support for Community Medicine Endowed Chair, Teikyo University School of Medicine, Tokyo, Japan
                [4 ]Human Physiology and Pathology, Faculty of Pharma Sciences, Teikyo University, Tokyo, Japan
                [5 ]Biomedical and Analytical Sciences, Faculty of Pharma Sciences, Teikyo University, Tokyo, Japan
                [6 ]Department of Organ Transplantation, Tianjin First Central Hospital, Tianjin, China
                University of São Paulo School of Medicine, BRAZIL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SU WXC. Performed the experiments: WXC. Analyzed the data: WXC SU. Contributed reagents/materials/analysis tools: SU WXC. Wrote the paper: SU. Supervision or mentorship: SU ZYS SF. Subject recruitment: TO YT TS TK SS YF SF. Interpretation of results: MH KK. Reviewed the manuscript and approved the final version: SU WXC TO YT TS TK SS YF MH KK ZYS SF.

                Article
                PONE-D-15-44867
                10.1371/journal.pone.0145506
                4689349
                26700005
                40476d4c-f5c4-41b3-a3df-ac6c9298ebe1
                © 2015 Uchida et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 12 October 2015
                : 4 December 2015
                Page count
                Figures: 4, Tables: 6, Pages: 16
                Funding
                This study was supported in part by a Grant-in-Aid for Progressive Renal Diseases Research, Research on Rare and Intractable Disease, from the Ministry of Health, Labour and Welfare of Japan (to SU) and Gout Research Foundation (to SU). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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