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

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.

Fig 1. The distribution of serum baseline uric acid and time-averaged uric acid in the CKD cohort.

Serum uric acid at the baseline and in the follow-up were normally distributed. The mean of the values were significantly different (6.5 ±1.4 vs. 6.7 ±1.3 mg/dL, p < 0.001).

Fig 2. The relationship between baseline uric acid and time-averaged uric acid.

The regression line is depicted in black line and a unity of the slope is overlapped in a dotted line. The correlation coefficients were 0.65 (p < 0.001). Two lines converge at serum uric acid 7.0 mg/dL. The time-averaged uric acid significantly decreased above the baseline uric acid 7.0 mg/dL and increased below the baseline uric acid 7.0 mg/dL.

Fig 3. Kaplan-Meier plots after the propensity score matching.

a) Time-averaged uric acid of 6.0 mg/dL, b) Time-averaged uric acid of 6.5 mg/dL, c) Time-averaged uric acid of 7.0 mg/dL. The patients at risk are shown below and p values are computed by stratified log-rank test.

Fig 4. Box plot of the propensity scores stratifying on the quintiles of the propensity score.

After trimming less than 2.5 percentile and greater than 97.5 percentile of the estimated propensity scores from the original cohort, a subsample was re-stratified on the quintiles of the propensity scores. In every substratum, the propensity scores are depicted by box-plots which represent the interquartile range and whiskers up to top most and the bottom most case within the 1.5 times the box length. Outliers appear as little circles. Estimated propensity scores between two groups separated by the threshold of time-averaged uric acid are well overlapped.