The effect of trajectory of serum uric acid on patients and renal outcomes in patients with stage-3 chronic kidney disease

Background Uric acid (UA) is associated with renal and patient survivals but the causal association in nature remains unclear. Also, no finding is yet available regarding longitudinal UA control (trajectory). Methods We enrolled 808 subjects diagnosed with stage 3 chronic kidney disease from 2007 to 2017. We plotted the mean UA over a period of 6 months with a minimum of three samples of UA was required. From the sampled points, we generated for each patient an interpolated line by joining mean values of the UA levels over time. And from the lines from all patients, we classified them into three groups of trajectories (low, medium and high) through group-based trajectory modeling, and then we further separated into either a treatment or no-treatment subgroups. Due to multiple comparisons, we performed post hoc analysis by Bonferroni adjustment. Using the univariate competing-risks regression, we calculated the competing risk analysis with subdistribution hazard ratio of possible confounders. Results appeared as gradually falling functions with time without any of the curves crossed over one another. For all-cause mortality risk, none of the variables (including age, gender, coronary arterial disease, cerebrovascular disease, diabetes mellitus, renin-angiotensin-aldosterone system inhibitors, trajectories of UA, and treatment of UA) was statistically significant. All 6 trajectories appeared as steady curve without crossovers among them over the entire period of follow-up. Patients with DM were statistically more likely to undergo dialysis. There was only a trend that the on-treatment trajectories, compared to their no-treatment trajectories, had lower risks for dialysis. There was no effect of UA control on patients’ survival. Conclusions Initial treatment of UA is utterly important for UA control. However, the long-term effects on patients and renal survivals maybe minor without statistical significance. uric acid, patient survival, renal survival, long-term effect, trajectory, competing risk


Abstract Background
Uric acid (UA) is associated with renal and patient survivals but the causal association in nature remains unclear. Also, no finding is yet available regarding longitudinal UA control (trajectory). Methods We enrolled 808 subjects diagnosed with stage 3 chronic kidney disease from 2007 to 2017. We plotted the mean UA over a period of 6 months with a minimum of three samples of UA was required. From the sampled points, we generated for each patient an interpolated line by joining mean values of the UA levels over time. And from the lines from all patients, we classified them into three groups of trajectories (low, medium and high) through group-based trajectory modeling, and then we further separated into either a treatment or no-treatment subgroups. Due to multiple comparisons, we performed post hoc analysis by Bonferroni adjustment. Using the univariate competing-risks regression, we calculated the competing risk analysis with subdistribution hazard ratio of possible confounders. Results All of the 6 trajectories appeared as gradually falling functions with time without any of the curves crossed over one another. For all-cause mortality risk, none of the variables (including age, gender, coronary arterial disease, cerebrovascular disease, diabetes mellitus, renin-angiotensinaldosterone system inhibitors, trajectories of UA, and treatment of UA) was statistically significant. All 6 trajectories appeared as steady curve without crossovers among them over the entire period of follow-up. Patients with DM were statistically more likely to undergo dialysis. There was only a trend that the on-treatment trajectories, compared to their no-treatment trajectories, had lower risks for dialysis. There was no effect of UA  [3,4], while other investigators disagree on any causal association. In this context, the association between hyperuricemia and mortality in patients with CKD is not yet determined [5]. As for the association with renal survival, no consensus is found [6][7][8][9]

Study population
We conducted this retrospective study in a medical center in central Taiwan. A flow chart of patients' inclusion and exclusion is summarized in Figure S1 (supplementary data).
From 2007 to 2017, outpatients with stage 3-CKD aged >20 years old were enrolled.
Patients who had died within two years after the enrollment were excluded. We calculated that every mean UA level from UA samples measured within 6 months. We required at least three samples to generate the mean UA used for our analysis. Finally, 808 subjects were successfully enrolled for this study. This study was approved by Ethics Committee of Taichung Veterans General Hospital (the number of institutional review board CE16235A).
All methods were carried out in accordance with relevant guidelines and regulations and all participants signed informed consents.

Data collection and outcome assessment
All data were retrospectively collected from medical records of patients. Tests of renal function were serum creatinine (SCr) level (mg/dl) and eGFR (ml/min/1.732m 2 ) (The eGFR was calculated by the equation of modification of diet of renal disease) [13]. Other demographic and laboratory data were also collected from medical records, including systolic blood pressure (SBP), DBP, glycated hemoglobin (HbA1c), total cholesterol, triglyceride, UA, hematocrit, and alanine transaminase (ALT).
The primary outcome is all-cause mortality. The CVD, coronary arterial disease (CAD), and congestive heart failure (CHF) were defined as previous CV outcome trial in type 2 diabetes mellitus (DM) [14]. The renal death was defined if patients should undergo regular course of dialysis for 30 or more days [15]. Medication of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs), and medications used for gout were collected if the duration of prescriptions was longer than three months.

Statistical methods
Continuous variables were reported as means ± SDs and categorical data were as numbers (percentages). Statistical significances across trajectories were determined using the Chi-square test for categorical variables, or one-way analysis of variance for continuous variables. To evaluate the UA trajectory, we used group-based trajectory modeling analysis, which is a statistical methodology to analyze developmental trajectories -the evolution of an outcome over time [16]. This analysis is typically used to describe the data with a time-based dimension to provide the empirical foundation for analyzing developmental trajectories. It can be used to identify unique subgroups within a cohort with participants following the same temporal trajectory [17]. It can be also used to analyze developmental trajectories of distinct but related behaviors (group-based method) [17]. It is the alternative method for analyzing the longitudinal data to evaluate outcomes [18]. We used this method to identify optimal groups of UA trajectory over time (Supplementary data, S2. detailed methods of model building process).
Cox's proportional hazards model was used to compare the differences of all-cause mortality, dialysis, and either one of them among different UA trajectories. As for dialysis, due to competing risk of death and dialysis, we used competing risk analysis as a sensitivity test for dialysis [19]. Competing risk analysis with subdistribution hazard ratio (SHR) and 95% confidence interval (95% CI) of the SHR of possible confounders were calculated by using competing-risks regression [11]. This model was used to determine factors confounding patient death to renal death.
All statistical analyses were performed using the SAS software, version 9.4 (SAS Institute,

Longitudinal data of long-term UA treatment
A total of 5742 patients of stage 3-CKD were enrolled, and among them, 808 patients were analyzed in this study (figure S1  figure 2B and Log HR=0.72 ((95% CI=0.149-1.292) in figure 2D). Even without statistical significances, there was a trend that "on-treatment" trajectory across all 3 UA trajectories had lower risks for dialysis, when compared to all 3 "no-treatment" trajectory counterparts ( figure 2D).

Discussion
In the general population, serum UA level is associated with CVD [3,4], and UA is considered an independent risk factor of CV mortality [20]. UA may be involved in the pathogenesis of CVD, but the causal relationship between UA and CVD remains unclear [21]. The situation is similar to the relationship between UA and renal injury [10,22]. Besides, the association between CVD and renal injury in patients with CKD is further rare [5] and the exact relationship has no consensus. In addition to UA level, the association between UA variability and patient survival or renal survival is further not well-studied [12]. In other words, the long-term effect and longitudinal tendency of UA is not known. The strength of our present study is that we have clarified the relationship between long-term effect of UA and patient or renal survival in the CKD groups. Currently, our study is the first one to research the trajectory of UA to patients and renal survivals.
The treatment response rates of allopurinol and febuxostat are around 40% and 70%, respectively [23]. In our study, the controlled levels of all three "on-treatment" trajectories were as follows: 9.6% in the low UA group ( 7.03±2.25 mg/dl), 76.2% in the medium UA group (8.14±2.1 mg/dl), and 14.2% in the high UA group (9.2±1.58 mg/dl).
As shown in figure 1, 6 trajectories of UA did not cross one another during the entire period of follow-up, which indicated that the treatment response was very rapid to achieve the stable level. As a result, there was no crossing over the curves. This finding is compatible with the studies on the pharmacodynamics of allopurinol, febuxosate and uricosuric agents [24]. According to the prescription guideline of allopurinol, the drop in serum UA level begins on day 2 before reaching the peak on day 7. The normal serum UA levels can be achieved typically within 1 to 3 weeks. Similarly, the peak UA-lowering effect of febuxostat also appears during the first 5 to 7 days of treatment. Therefore, the longterm effect of UA control is based on the treatment decision (treat or not treat) during the first few weeks.
In the group of low UA trajectory, participants had better renal function (lower SCr) (p=0.001), lower SBP (p=0.028), less UA (p<0.0001), less ALT (p=0.040), fewer deaths (p=0.032) and fewer receiving dialysis (p=0.019). These findings suggested that patients in this group had the lowest risk for metabolic syndrome and oxidative stress. Moreover, we chose low UA-no treatment trajectory as the reference group for analysis instead of low UA-on treatment trajectory. This is because the trajectory of low UA-on treatment must have hyperuricemia before UA-lowering agents. Those patients had already experienced higher risk of metabolic syndrome and oxidative stress before the treatment.
The risk of metabolic disease for "on-treatment" trajectory should be higher than for "notreatment" trajectory. In summary, choosing low UA-no treatment trajectory as reference group is reasonable.
The pleotropic effect of UA-lowering agents is still under debate. In animal models, xanthine oxidase may cause kidney fibrosis through inflammation, endothelial dysfunction, oxidative stress, and activation of the renin-angiotensin system [25]. In some studies, both allopurinol [26][27][28][29] and febuxosate [30][31][32][33][34] show renal protections independent from their UA lowering effect. Our present results did not support renal protection of the UAlowering agents with statistical significance. There was only a trend that "on-treatment" trajectory across all 3 UA trajectories had lower risks for dialysis, when compared to all 3 "no-treatment" trajectory counterparts ( figure 2D). However, the same trend was not observed regarding the all-cause mortality. Our study is the first one to indicate the longterm effect of UA control on patients and renal survivals. The long-term benefit of UA control maybe much minor than the control of BP, hyperlipidemia and DM.
There are some limitations of this study. First, detailed medication data were not available. However, regarding the therapy for our patients with stage 3-CKD, xanthine oxidase inhibitor therapy is the consensus first-line treatment in line with previous studies [35] and guidelines in Taiwan [36]. The effect of this limitation may be not large. Second, only patients surviving ≥ 2 years from the time of enrolment were included in this study.
This could imply minor bias toward good adherence to medical follow-ups. Third, this is a retrospective cohort study on a heterogeneous population. It still needs more prospective studies to confirm the long-term effect of UA variability on patients and renal outcome.

Conclusion
Earlier treatment for hyperuricemia is important for UA control due to rapid response of medications. However, the benefit on patients and renal outcomes of UA control maybe minor in the long-term follow-up without confounded by patient death.

Ethics approval and consent to participate: This study was approved by Ethics
Committee of Taichung Veterans General Hospital, IRB number CE16235A. All methods were carried out in accordance with relevant guidelines and regulations and all participants signed informed consents.

Consent to publish: Not applicable
Availability of data and materials: The individual patient-level data was not made publically available due to containing potentially identifying patient data; however, the study data may be made available from the authors upon reasonable request.

Competing interests:
The author declare they have no competing interests.  Tables   Table 1 Baseline   Values are means ± SD or n (%); BP: blood pressure; HbA1c: glycated hemoglobin;, eGFR:

Supplementary Files
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