Abstract
Background:
Epidemiological studies have suggested that elevated serum uric acid may contribute to the progression of chronic kidney disease. However, no large prospective study has examined whether hyperuricemia is an independent risk factor for the progression of autosomal dominant polycystic kidney disease (ADPKD).
Methods:
We measured uric acid in stored serum samples from the 2-year study visit in 671 participants from the HALT PKD multicenter trials. Participants were categorized according to uric acid tertiles. For Study A (participants aged 15-49 years with preserved kidney function, n=350) we used linear mixed effects models to examine the association between uric acid and repeated measures of height-adjusted total kidney volume (htTKV), the primary outcome for Study A. For Study B (participants aged 18-64 with decreased kidney function, n=321) we used Cox proportional hazards models to assess the hazard for the combined endpoint of 50% loss in estimated glomerular filtration rate (eGFR), end-stage kidney disease (ESKD), or death, the primary outcome for Study B. To assess the association of uric acid with the slope of eGFR decline (secondary outcome of HALT A and B) we used linear mixed effects models in the combined population of Study A and B.
Results:
In the unadjusted model, the annual change in htTKV was 2.7% higher in the highest uric acid tertile compared to the lowest (p<0.001), but this difference became insignificant after adjustment for gender. Men had faster TKV growth than women (p<0.001). There was no difference in eGFR decline between the 3 uric acid tertiles. Hazard ratios for the clinical endpoint were 2.9 (95% confidence interval, 1.9-4.4) and 1.8 (1.1-2.8) respectively in the high and medium uric acid groups in unadjusted and partially adjusted models (p<0.001), but the significance was lost after adjustment for baseline eGFR. Results were similar when uric acid was examined as a continuous variable.
Conclusion:
Elevated serum uric acid is not an independent risk factor for disease progression in ADPKD.
Keywords: Autosomal dominant polycystic kidney disease, serum uric acid, total kidney volume, estimated glomerular filtration rate, HALT PKD trials, chronic kidney disease
Graphical Abstract
INTRODUCTION:
Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited renal disorder leading to end-stage kidney disease (ESKD) in the majority of afflicted individuals, with an estimated prevalence of 1 in 1000 to 1 in 2,500 in populations worldwide [1–3]. The clinical course is quite variable even within families who have the same underlying germ line gene mutation, and ESKD can occur between childhood and old age, or not at all. Great progress has been made in recent years toward understanding the genetic basis of the disease and the pathophysiology of cystic growth, resulting in the Food and Drug Administration (FDA) approval of tolvaptan, the first treatment for ADPKD in patients who are at risk for rapid progression [4]. However, tolvaptan has significant side effects including the potential for severe liver damage. Therefore, there is still a pressing need for a well-tolerated drug that can be given for a prolonged period of time.
Clinical risk factors for more rapid progression are male gender, early onset of hypertension, early occurrence of gross hematuria and/or multiple episodes of hematuria, lower high-density lipoprotein (HDL) cholesterol levels, and possibly hyperuricemia [5–8]. Elevated uric acid levels stimulate the renin-angiotensin-aldosterone system (RAAS) and reduce endothelial nitric oxide availability [9,10]. Vascular inflammation and endothelial dysfunction are early manifestations of ADPKD [11,12] and may be reversible by treatment with allopurinol, shown to be effective in a randomized study of subjects with normal renal function [13]. High intracellular uric acid levels also exert oxidant and inflammatory effects [14–16], which are important factors for renal function loss in ADPKD [17,18]. To study the potential contribution of hyperuricemia to the progression of ADPKD we undertook a secondary analysis of the multicenter HALT PKD trials.
The HALT PKD trials (ClinicalTrials.gov numbers NCT00283686 for Study A and NCT01885559 for Study B) were randomized, double-blind, placebo-controlled trials to test the hypothesis that intensive blood pressure (BP) control using single or double-agent RAAS blockade will slow renal cyst growth as well as decline of estimated glomerular filtration rate (eGFR) in hypertensive patients with ADPKD. Design and primary results have been published [19–21]. Because the HALT trials excluded subjects with significant comorbidities including diabetes, and hypertension was treated meticulously in all participants, the association of serum uric acid (SUA) with outcomes can be assessed without confounding by uncontrolled hypertension and other comorbidities.
SUBJECTS AND METHODS
HALT Study population:
HALT Study A randomized 558 young (15-49 years, mean age 36 ± 8 years) subjects with preserved renal function (eGFR > 60 mL/min/1.73 m2) in a 2x2 factorial design to either a low (95/60-110/75 mm Hg) or standard BP goal (120/70-130/80 mm Hg) using either lisinopril and placebo or the combination of lisinopril and telmisartan, with other medications added as needed to achieve the BP goal [20]. Study B randomized 486 older (18-64 years, mean age 48 ± 8 years) patients with reduced eGFR (25-60 mL/min/1.73 m2) to either lisinopril and placebo or lisinopril and telmisartan to achieve a single BP goal of 120-130/70-80 mm Hg [21]. All participants gave informed consent and the trials were conducted according to the principles of the Helsinki Declaration. They were approved by the Colorado Multiple Institutional Review Board (COMIRB) with the COMIRB Protocol Number 02-744 and the individual review boards of all study centers.
Participants were evaluated at 7 study centers at baseline, at 4, 7 and 12 months, and then every 6 months until the end of the trial in 2014 (Study A) or until a subject met an endpoint (Study B). At each study visit all concomitant medications were documented and entered into the database. Cardiac and renal magnetic resonance imaging (MRI) was obtained at baseline and after 2, 4 and 5 years in Study A participants using methods established by the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) [22]. Estimated GFR was calculated for both Study A and B using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, based on centralized serum creatinine determinations by isotope dilution mass spectrometry [23].
The primary outcome for Study A was percent change in TKV over 5 years, for Study B the composite of time to ESRD (initiation of dialysis or renal transplant), death, or 50% reduction in eGFR. Secondary outcomes were rate of change in eGFR, and for Study A change in albuminuria, renal blood flow and left ventricular mass index, both measured by MRI. Follow-up time was 5-8 years.
Exposure variable and study population for this secondary analysis:
Serum uric acid, the exposure variable, was not measured as part of the HALT protocol. We had stored serum samples available from the 2-year study visit of 465 Study A and 403 Study B participants (see Figure). These were used to measure uric acid levels in the Clinical Laboratories of University of Colorado Hospital with the Beckman Coulter AU5800 analyzer, based on the modified Fossati method. 115 Study A and 82 Study B participants did not have complete data on covariates or follow-up information available and were therefore excluded from this analysis, leaving 350 subjects in Study A and 321 in Study B. One subject in Study A did not have a 5-year MRI and was therefore excluded from the TKV analysis. Five Study B participants did not have complete data on the combined endpoint and were excluded from the event analysis but contributed data to the eGFR slope analysis. SUA was assessed as a categorical variable (tertiles) for each outcome. We also examined the association of SUA as a continuous variable with each outcome.
Figure 1:
Flow diagrams of included and excluded participants in HALT Studies A and B.
A: Of 558 randomized Study A participants, 465 had a serum sample available at the 2-year study visit for uric acid determination. Of those, 115 were excluded due to missing data on covariates or follow-up measures. After 3 years of follow-up, one subject did not have an MRI at 5 years and was excluded from the TKV analysis (but not the eGFR analysis).
B: Of 486 randomized Study B participants, 403 had a serum sample available at the 2-year study visit for uric acid determination. Of those, 82 were excluded due to missing data on covariates or follow-up measures. After 3-6 years of follow-up, 5 subjects did not have complete data about an endpoint and were excluded from the events analysis (but not the eGFR analysis).
Follow-up:
The 2-year visit is considered baseline for this analysis, with subjects followed until the end of the trials. Follow-up time was 3 years for the TKV analysis and 3-6 years for the eGFR and events analysis.
Outcomes:
Primary outcomes for this secondary analysis were height-adjusted total kidney volume (htTKV) in Study A (TKV analysis, 349 participants with available MRI) and the combined endpoint of ESKD, death or 50% reduction in eGFR in Study B (events analysis, 316 participants with complete data available). The third outcome was eGFR slope, assessed in the larger population of Study A and B combined (eGFR analysis, 671 participants).
Covariates:
Age, sex, race, body mass index (BMI), systolic and diastolic blood pressure, eGFR, and 24-hour urinary albumin excretion were all determined during the baseline visit for this analysis. Current smoking status, assignment to the low or standard BP arm of Study A (BP arm), assignment to single or double RAAS blockade (treatment arm), and current treatment with diuretics or allopurinol were also considered. The PKD genotype was determined in the laboratory of Dr. Peter Harris as described by Heyer at al. [24] and was also added as a confounding variable. Four categories were considered: PKD1 truncating mutations (PKD1-T), PKD1 nontruncating mutations (PKD1-NT), PKD2 mutations (PKD2), and “no mutation detected” (NMD).
Statistical analysis:
Demographic and laboratory baseline characteristics were compared across tertiles of baseline SUA levels using the Chi-Square test for categorical variables and analysis of variance (ANOVA) for continuous variables. The rank sum test was used if appropriate. Log transformation was applied to htTKV and urinary albumin excretion due to their skewed distribution.
Linear mixed effects models with random intercept and slopes were used to examine the association between SUA and htTKV as well as eGFR, similar to the main analyses of the HALT PKD trials [20,21]. The Cox proportional hazard models were used to examine the relationship of SUA with the combined event (ESKD, death or 50% reduction in eGFR) in Study B. We tested the association of SUA with outcomes in an unadjusted analysis (model 1) and then in adjusted models: in model 2 we adjusted for age, sex, race, PKD genotype, BP arm, treatment arm, BMI, systolic and diastolic BP, 24-hour urine albumin excretion, and current smoking, all at month 24 which was baseline for this secondary analysis. Model 3 was further adjusted for allopurinol and diuretic use at month 24, and model 4 was further adjusted for eGFR at month 24. In the analysis of htTKV, htTKV at 2 years was retained as measure of the outcome in the mixed model and the 2-year eGFR was considered a covariate (natural log eGFR was used because of better model fit). The natural log value of htTKV was used in the analysis, and the slope was converted into annual percent change using the formula as in the main analyses of the HALT PKD trials [20]. In the analysis of eGFR, the eGFR at 2 years was retained as measure of the outcome in the mixed model, not as covariate.
With mixed effects analysis of longitudinal data presented here, the interaction effect of SUA and time is the key answer to assess a relationship of interest, i.e. how SUA associates with the change in the outcome variable over time (i.e. slope of time). In the analysis of mixed effects models with adjustment for a covariate, the effects of the covariate on both the intercept and slope were considered by including both the covariate and its interaction with time in the models.
Because a strong association of male sex with steeper slope of htTKV increase was found, we further conducted sex-specific subgroup analyses of the effects of SUA on htTKV and eGFR. Due to smaller numbers, we dichotomized SUA based on sex-specific clinical definitions of hyper- and normouricemia. Hyperuricemia was defined as SUA ≥ 7 mg/dL in males and ≥ 6 mg/dL in females. All statistical analyses were performed using SAS software version 9.4.
RESULTS
TKV analysis (Study A, n=349):
Baseline characteristics of participants included in the htTKV analysis are shown in Table 1. Those in the high SUA tertile were predominantly (76%) male and had higher BMI than those in the lower tertiles. They also had lower eGFR and higher htTKV at baseline. Age, systolic and diastolic BP, assignment to the low and standard BP arm, single and double agent treatment arm, diuretic or allopurinol use, and distribution of PKD genotypes were not different between SUA tertiles.
Table 1:
Baseline characteristics of Study A participants included in TKV analysis
| Characteristic | Total (n= 349) | Serum uric acid (mg/dL) | P value | ||
|---|---|---|---|---|---|
| Low (≤ 5.1) (n= 117) | Medium (5.11-6.79) (n= 113) | High (≥ 6.8) (n= 119) | |||
| Age (years), mean (std) | 40 (8) | 39 (9) | 39 (8) | 41 (7) | 0.39 |
| White, n (%) | 329 (94) | 109 (93) | 109 (96) | 111 (93) | 0.48 |
| Male, n (%) | 178 (51) | 18 (15) | 70 (62) | 90 (76) | < 0.001 |
| BMI (kg/m2), mean (std) | 27.3 (4.8) | 25.9 (5.2) | 27.0 (4.1) | 29.0 (4.7) | < 0.001 |
| SUA (mg/dL), mean (std) | 6.2 (2.0) | 4.1 (0.6) | 5.9 (0.4) | 8.4 (1.5) | < 0.001 |
| SBP (mmHg), mean (std) | 114 (13) | 113 (12) | 115 (12) | 115 (14) | 0.58 |
| DBP (mmHg), mean (std) | 73 (10) | 73 (10) | 73 (10) | 73 (10) | 0.97 |
| eGFR (mL/min/1.73 m2), mean (std) | 84 (20) | 93 (19) | 86 (19) | 74 (19) | < 0.001 |
| htTKV (mL/m), median (IQR) | 596 (404-896) | 515 (389-718) | 601 (398-855) | 700 (492-1098) | < 0.001 |
| Ualbum (mg/24 hours, median (IQR) | 13 (7-25) | 15 (7-30) | 10 (7-18) | 12 (8-27) | 0.03 |
| GenoType | 0.73 | ||||
| PKD1-T, n (%) | 161 (46) | 58 (50) | 51 (45) | 52 (44) | |
| PKD1-NT, n (%) | 96 (28) | 25 (21) | 33 (29) | 38 (32) | |
| PKD2, n (%) | 64 (18) | 24 (21) | 20 (18) | 20 (17) | |
| NMD, n (%) | 28 (8) | 10 (9) | 9 (8) | 9 (8) | |
| Low BP, n (%) | 165 (47) | 47 (40) | 57 (50) | 61 (51) | 0.17 |
| Treatment, n (%) | 179 (51) | 59 (50) | 55 (49) | 65 (55) | 0.65 |
| Current smoking, n (%) | 32 (9) | 8 (7) | 10 (9) | 14 (12) | 0.42 |
| Diuretic use, n (%) | 33 (9) | 8 (7) | 14 (12) | 11 (9) | 0.35 |
| Allopurinol use, n (%) | 11 (3) | 2 (2) | 3 (3) | 6 (5) | 0.32 |
BMI: body mass index; DBP: diastolic blood pressure; eGFR: estimated GFR using the CKD-EPI equation; htTKV: height-adjusted total kidney volume; IQR: interquartile range; Low BP: randomized to the low blood pressure goal; NMD: no mutation detected; PKD1-T: truncating PKD1 mutation; PKD1-NT: nontruncating PKD1 mutation; PKD2: PKD2 mutation; SBP: systolic blood pressure; std: standard deviation; SUA: serum uric acid; treatment: randomized to lisinopril + telmisartan; Ualbum: urinary albumin excretion.
The P values are based on analysis of variance (ANOVA) for continuous variables and the Chi-square test for categorical variables. For Ualbum the log value was used.
In the unadjusted model annual percent increase in htTKV varied significantly among the three SUA groups (p < 0.0001); the high and medium SUA groups had 2.7% (high) and 1.6% (medium) greater annual increase in htTKV than the low SUA group, suggesting a significant uric acid association with htTKV increase (Table 2A). However, the association was attenuated to non-significant after including the interaction term of sex by time, i.e. after adjusting for the sex effect on the slope of htTKV (p = 0.19). Men had faster TKV growth than women (p < 0.0001), indicating that the observed relationship between higher SUA and faster htTKV increase might be due to the male preponderance among participants with high SUA levels. Adjustment for all other covariates further attenuated the uric acid association and supported the conclusion that SUA had no independent relationship with htTKV increase (Table 2A).
Table 2:
Results from analysis with mixed effect models for HtTKV and eGFR
| 2A: Annual percent change (increase) in HtTKV (95% CI) with tertiles of serum uric acid as predictor | ||||
|---|---|---|---|---|
| Assessment | Model 1 | Model 2 | Model 3 | Model 4 |
| Difference in annual HtTKV % change, medium vs low | 1.6 (0.5-2.7) | 0.3 (−0.8-1.4) | 0.2 (−0.9-1.3) | 0.0 (−1.1-1.0) |
| Difference in annual HtTKV % change, high vs low | 2.7 (1.6-3.7) | 0.6 (−0.6-1.8) | 0.6 (−0.6-1.8) | −0.3 (−1.5-0.9) |
| Difference in annual HtTKV % change, high vs medium | 1.1 (0.0-2.2) | 0.3 (−0.7-1.3) | 0.4 (−0.7-1.4) | −0.2 (−1.3-0.8) |
| 2B: Annual change (decrease) in eGFR (95% CI) with tertiles of serum uric acid as predictor | ||||
| Assessment | Model 1 | Model 2 | Model 3 | N/A |
| Difference in annual eGFR change (mL/min/1.73 m2), medium vs low | −0.2 (−0.6-0.3) | 0.0 (−0.5-0.5) | 0.0 (−0.4-0.5) | |
| Difference in annual eGFR change (mL/min/1.73 m2), high vs low | −0.3 (−0.7-0.2) | −0.0 (−0.6-0.5) | 0.0 (−0.5-0.6) | |
| Difference in annual eGFR change (mL/min/1.73 m2), high vs medium | −0.1 (−0.6-0.4) | −0.0 (−0.5-0.5) | −0.0 (−0.5-0.5) | |
Model 1: unadjusted.
Model 2: adjusted for age, sex, race, body mass index, PKD genotype, study group (low/standard blood pressure), treatment group (lisinopril + telmisartan/lisinopril + placebo), systolic blood pressure, diastolic blood pressure, 24-hour urine albumin excretion, and current smoking (at month 24).
Model 3: further adjusted for diuretic use and allopurinol use at month 24.
Model 4: further adjusted for eGFR at month 24 (only for HtTKV analysis).
The sex-specific subgroup analysis included 178 males and 171 females. According to the clinical definition of hyperuricemia, 79 (44%) males were hyperuricemic (at the 24-month study visit of HALT), but only 41 (24%) women had hyperuricemia. There was a 1.6% greater annual increase in htTKV in hyperuricemic compared to normouricemic men in the unadjusted model (p = 0.01), but no difference between high and low SUA males in the fully adjusted model (p = 0.55, data not shown). For women we found no significant difference between the high and low SUA groups in all models tested.
When modeling SUA as a continuous variable, we found that it was associated with TKV increase in males in models that did not include baseline eGFR as a covariate (p = 0.02), but this association became non-significant after adjustment for kidney function (p = 0.43). In females there was no association between increasing SUA and TKV growth in either unadjusted (p = 0.83) or fully adjusted (p = 0.16) models. In the entire sample SUA as a continuous variable was not associated with TKV increase after adjustment for covariates (p = 0.85). Taken together, this confirms that SUA is not associated with the rate of TKV increase.
eGFR analysis (Study A and B combined, n=671):
Baseline characteristics of participants included in the eGFR slope analysis are shown in Table 3. Subjects in the high tertile of SUA were significantly older, predominantly (70%) male, with higher BMI, higher urine albumin excretion and lower eGFR at baseline. Systolic and diastolic BP, PKD genotype, current smoking, treatment assignment, and diuretic or allopurinol use were not different between the 3 groups.
Table 3:
Baseline characteristics of participants included in eGFR analysis (study A and B combined)
| Characteristic | Total (n= 671) | Serum uric acid (mg/dL) | P value | ||
|---|---|---|---|---|---|
| Low (≤ 5.7) (n= 230) | Medium (5.71-7.39) (n= 214) | High (≥7.4) (n= 227) | |||
| Age (years), mean (std) | 45 (10) | 44 (10) | 45 (10) | 47 (10) | < 0.01 |
| White, n (%) | 635 (95) | 218 (95) | 205 (96) | 212 (93) | 0.53 |
| Male, n (%) | 333 (50) | 54 (23) | 119 (56) | 160 (70) | < 0.001 |
| BMI (kg/m2), mean (std) | 27.6 (5.0) | 26.2 (4.8) | 27.4 (4.6) | 29.1 (5.1) | < 0.001 |
| SUA (mg/dL), mean (std) | 6.7 (2.0) | 4.6 (0.8) | 6.5 (0.4) | 9.0 (1.4) | < 0.001 |
| SBP, mean (std) | 116 (12) | 115 (12) | 117 (12) | 117 (13) | 0.10 |
| DBP, mean (std) | 74 (9) | 74 (10) | 74 (9) | 75 (9) | 0.56 |
| eGFR (mL/min/1.73 m2), mean (std) | 63 (28) | 78 (26) | 62 (27) | 50 (23) | < 0.001 |
| Ualbum (mg/24 hours), median (IQR) | 17 (8-39) | 15 (7-29) | 16 (8-37) | 23 (10-62) | < 0.001 |
| GenoType | 0.04 | ||||
| PKD1-T, n (%) | 335 (50) | 112 (49) | 108 (51) | 115 (51) | |
| PKD1-NT, n (%) | 181 (27) | 48 (21) | 65 (30) | 68 (30) | |
| PKD2, n (%) | 111 (17) | 49 (21) | 30 (14) | 32 (14) | |
| NMD, n (%) | 44 (7) | 21 (9) | 11 (5) | 12 (5) | |
| Treatment, n (%) | 339 (51) | 113 (49) | 110 (51) | 116 (51) | 0.87 |
| Current smoking, n (%) | 51 (8) | 14 (6) | 16 (7) | 21 (9) | 0.44 |
| Diuretic use, n (%) | 94 (14) | 24 (10) | 33 (15) | 37 (16) | 0.15 |
| Allopurinol use, n (%) | 39 (6) | 12 (5) | 9 (4) | 18 (8) | 0.22 |
BMI: body mass index; DBP: diastolic blood pressure; eGFR: estimated GFR using the CKD-EPI equation; IQR: interquartile range; NMD: no mutation detected; PKD1-T: truncating PKD1 mutation; PKD1-NT: nontruncating PKD1 mutation; PKD2: PKD2 mutation; SBP: systolic blood pressure; std: standard deviation; SUA: serum uric acid; treatment: randomized to lisinopril + telmisartan; Ualbum: urinary albumin excretion.
The P values are based on analysis of variance (ANOVA) for continuous variables and the Chi-square test for categorical variables. For Ualbum the log value was used.
There was no significant difference in annual decline of eGFR between the 3 SUA groups, either unadjusted or adjusted for covariates (Table 2B). SUA had no association with the rate of eGFR decline.
The sex-specific subgroup analysis included 333 males and 338 females. Using the clinical definition of hyperuricemia, we found that 183 (55%) men and 143 (42%) women had hyperuricemia, respectively. There was no significant difference in eGFR decline between high and low SUA in either men or women in all models tested.
Examining SUA as continuous variable we found no association with slope of eGFR decline in either men or women or in the entire sample, in either unadjusted or fully adjusted models.
Events analysis (Study B, n=316):
Baseline characteristics of participants included in the events analysis are shown in Table 4. Subjects in the high SUA tertile were 68% male and had higher BMI, higher urine albumin excretion and lower baseline eGFR than those in the low tertile. Age, systolic and diastolic BP, genotype distribution, current smoking, and diuretic and allopurinol use were not different between groups.
Table 4:
Baseline characteristics of Study B participants included in event analysis
| Characteristic | Total (n= 316) | Serum uric acid (mg/dL) | P value | ||
|---|---|---|---|---|---|
| Low (≤ 6.4) (n= 111) | Medium (6.41-7.99) (n= 95) | High (≥ 8.0) (n= 110) | |||
| Age (years), mean (std) | 51 (8) | 52 (7) | 51 (8) | 51 (8) | 0.34 |
| White, n (%) | 300 (95) | 110 (99) | 86 (91) | 104 (95) | 0.02 |
| Male, n (%) | 151 (48) | 27 (24) | 49 (52) | 75 (68) | < 0.001 |
| BMI (kg/m2), mean (std) | 27.9 (5.2) | 26.7 (4.9) | 28.3 (5.8) | 28.7 (4.7) | < 0.01 |
| SUA (mg/dL), mean (std) | 7.3 (1.9) | 5.4 (0.7) | 7.1 (0.5) | 9.3 (1.3) | < 0.001 |
| SBP (mmHg), mean (std) | 119 (11) | 119 (10) | 118 (11) | 120 (12) | 0.60 |
| DBP (mmHg), mean (std) | 75 (8) | 76 (9) | 74 (7) | 77 (9) | 0.02 |
| eGFR (mL/min/1.73 m2), mean (std) | 40 (13) | 46 (13) | 41 (13) | 35 (11) | < 0.001 |
| Years of follow-up, median (IQR) | 3.6 (2.5-4.7) | 4.0 (3.1-5.1) | 3.7 (2.3-4.6) | 3.1 (1.9-4.2) | < 0.001 |
| Ualbum (mg/24 hours, median (IQR) | 25 (11-62) | 22 (10-47) | 22 (9-62) | 39 (12-82) | 0.02 |
| GenoType | 0.92 | ||||
| PKD1-T, n (%) | 171 (54) | 59 (53) | 54 (57) | 58 (53) | |
| PKD1-NT, n (%) | 83 (26) | 27 (24) | 26 (27) | 30 (27) | |
| PKD2, n (%) | 47 (15) | 18 (16) | 12 (13) | 17 (15) | |
| NMD, n (%) | 15 (5) | 7 (6) | 3 (3) | 5 (5) | |
| Treatment, n (%) | 156 (49) | 51 (46) | 57 (60) | 48 (44) | 0.04 |
| Current smoking, n (%) | 19 (6) | 8 (7) | 5 (5) | 6 (5) | 0.80 |
| Diuretic use, n (%) | 61 (19) | 18 (16) | 19 (20) | 24 (22) | 0.56 |
| Allopurinol use, n (%) | 28 (9) | 9 (8) | 8 (8) | 11 (10) | 0.87 |
BMI: body mass index; DBP: diastolic blood pressure; eGFR: estimated GFR using the CKD-EPI equation; IQR: interquartile range; NMD: no mutation detected; PKD1-T: truncating PKD1 mutation; PKD1-NT: nontruncating PKD1 mutation; PKD2: PKD2 mutation; SBP: systolic blood pressure; std: standard deviation; SUA: serum uric acid; treatment: randomized to lisinopril + telmisartan; Ualbum: urinary albumin excretion.
The P values are based on analysis of variance (ANOVA) for continuous variables and the Chi-square test for categorical variables. For Ualbum the log value was used.
The rank sum test was used to compare years of follow-up between groups.
Serum uric acid was strongly associated with events (combined endpoint of 50% decline in eGFR, ESKD or death) in the unadjusted model (p < 0.0001) and in models adjusted for demographic and clinical parameters (models 2 and 3, p < 0.0001 in both models), with a hazard ratio of 2.9 (95% confidence interval 1.9-4.4) in the high SUA tertile compared to the low tertile (Table 5). However, after further adjustment for baseline eGFR the SUA association with events was no longer significant (p = 0.59, Table 5). Similarly, using SUA as continuous variable, the risk of events significantly increased with SUA before adjustment for baseline eGFR (hazard ratio 1.25, 95% confidence interval 1.15-1.36, for each mg/dL SUA increase, p < 0.0001), but this association became non-significant after adjustment for baseline eGFR (hazard ratio 0.99, 95% confidence interval 0.90-1.09, p = 0.78). This indicates that SUA is not an independent risk factor for adverse events but rather a marker of advanced kidney disease.
Table 5:
Association of serum uric acid level with outcomes in Study B (Hazard ratio, 95% confidence intervals)
| Model | Serum uric acid categorized by tertiles | ||
|---|---|---|---|
| Low (≤ 6.4 mg/dL) | Medium (6.41-7.99 mg/dL) | High (≥ 8.0 mg/dL) | |
| 1 | 1.0 (ref) | 1.7 (1.1-2.6) | 2.9 (2.0-4.3) |
| 2 | 1.0 (ref) | 1.8 (1.1-2.8) | 2.9 (1.9-4.4) |
| 3 | 1.0 (ref) | 1.8 (1.1-2.8) | 2.9 (1.9-4.4) |
| 4 | 1.0 (ref) | 1.3 (0.8-2.0) | 1.1 (0.7-1.8) |
Outcomes (events): 50% decrease in eGFR, ESKD or death
Ref: reference group
Model 1: unadjusted.
Model 2: adjusted for age, sex, race, body mass index, PKD genotype, treatment group (lisinopril + telmisartan/lisinopril + placebo), systolic blood pressure, diastolic blood pressure, 24-hour urine albumin excretion, and current smoking (month 24).
Model 3: further adjusted for diuretic use and allopurinol use at month 24.
Model 4: further adjusted for eGFR at month 24.
DISCUSSION
In epidemiological studies hyperuricemia is associated with onset of chronic kidney disease in the general population [25,26] and in patients with diabetes [27,28]. In animal models hyperuricemia (induced by oxonic acid, a uricase inhibitor) stimulates the RAAS and causes microvascular renal damage, renal interstitial inflammation and fibrosis which can be prevented by treatment with a xanthine oxidase inhibitor [9,29,30]. The impact of serum uric acid on the progression of ADPKD has not been examined in a large prospective patient population. Therefore, we sought to determine whether higher serum uric acid levels are independently associated with faster progression in the cohort of HALT PKD trial participants with meticulously controlled hypertension using RAAS inhibitors, excluding confounding by poorly controlled blood pressure. We found that serum uric acid did not have an independent association with kidney volume growth or eGFR decline, nor with the combined endpoint of ESKD, 50% decrease of eGFR or death in the HALT study population. Higher serum uric acid was closely correlated with lower eGFR, even in subjects with preserved baseline renal function (Study A), and thus a marker of more advanced disease.
Interestingly, in our initial analysis of TKV increase according to uric acid tertiles, participants in the higher tertiles did have a steeper slope of TKV increase, but this was entirely explained by male preponderance in these higher tertile groups. Men have faster TKV growth than women, which was also observed in the primary analyses of the HALT data [20] and in the TEMPO 3:4 (Tolvaptan Efficacy and Safety in Management of Autosomal Dominant Polycystic Kidney Disease and Its Outcomes 3:4) trial of tolvaptan therapy [4]. No gender effect was found for eGFR decline or for the combined endpoint of ESKD, death or 50% decrease in eGFR.
Whether serum uric acid level is an independent risk factor for renal disease progression has been examined in many clinical studies of various nephropathies. In a large clinical and histopathological study of 1070 renal biopsies, hyperuricemia was independently associated with the occurrence and degree of segmental glomerulosclerosis, tubular atrophy and interstitial fibrosis [31]. A prospective study of 670 Caucasian patients with type 1 diabetes and normal to mildly impaired renal function showed that uric acid was an independent risk factor for kidney function decline, cardiovascular events and all-cause mortality [32]. In a historical cohort study of 7033 Japanese patients with diabetes type 2, higher serum uric acid was independently associated with kidney function decline only in the group of patients (n = 4994) without impaired kidney function at baseline [33]. Another retrospective study from Japan in 12,578 subjects with normal renal function showed that higher baseline serum uric acid and increase in uric acid over time were independent risk factors for rapid eGFR decline [34]. A study of 5090 subjects with chronic kidney disease (CKD) from Taiwan found that elevated serum uric acid trajectories over time are associated with accelerated progression to ESRD and all-cause mortality, after multi-variable adjustments including eGFR [35]. Finally, the relationship between serum uric acid quartiles and subsequent kidney failure (initiation of renal replacement therapy) and death was examined in the Chronic Renal Insufficiency Cohort (CRIC) study, a prospective observational study of 3885 individuals with CKD in the United States [36]. The investigators found that during a median follow-up of 7.9 years, higher uric acid concentrations were independently associated with risk for kidney failure in subjects with mildly decreased kidney function (eGFR ≥ 45 mL/min/1.73 m2) but not in those with more advanced CKD; the relationship with all-cause mortality was J-shaped [36].
The results reported here contrast with an earlier retrospective study of 680 adult patients with ADPKD and mildly impaired renal function, in which hyperuricemia was associated with earlier onset of hypertension, larger kidneys and younger age at ESKD even after adjustment for age, sex, BMI, medications and baseline renal function [37]. However, that study relied on natural history data and questionnaires as opposed to a clinical trial population. A cross-sectional study was conducted in Turkey among 91 normotensive individuals with early-stage ADPKD (mean eGFR 86 mL/min/1.73 m2) and found that individuals with hyperuricemia had lower flow-mediated vasodilation, indicating endothelial dysfunction, than those with normal serum uric acid levels [38]. No large prospective study has addressed the role of serum uric acid for the progression of ADPKD.
There is no clear evidence that uric acid handling is different in ADPKD compared to other renal diseases. Whereas some, mostly older studies report a higher frequency of hyperuricemia and gout in ADPKD than in other kidney diseases, others have found no difference in urate handling or serum uric acid levels [39]. Interestingly, the gene for the urate transporter ABCG2 is located next to the gene for PKD2 on chromosome 4 [40], therefore it is possible that mutations in these genes are inherited together, causing gout in a small subset of patients with ADPKD. To our knowledge no data have been published on coinheritance of ABCG2 and PKD2 mutations.
Several retrospective data analyses and small randomized trials have suggested that treatment with xanthine oxidase inhibitors such as allopurinol or febuxostat may slow renal function decline in patients with diabetes [41] or with chronic kidney disease [42]. However, two recent randomized controlled trials in 467 patients with CKD stage 3 and 369 patients with CKD stages 3–4 were unable to show a benefit of uric acid lowering therapy on renal function decline [43,44]. Similarly, a double-blind, placebo-controlled, multicenter trial in 530 subjects with type 1 diabetes and mild to moderate diabetic kidney disease revealed that treatment with allopurinol for 2 years had no effect on the decline in measured GFR [45]. Our finding that uric acid is not an independent risk factor for ESKD or eGFR decline in ADPKD aligns with the results of these recent randomized controlled trials. Further support for our conclusion comes from Mendelian randomization studies, in which genetic variants that are known to influence SUA levels were not associated with chronic kidney disease or eGFR among several hundred thousand subjects studied, arguing against a causal role of SUA for kidney disease in the general population [46,47].
Limitations of this analysis are the lack of uric acid measurements during follow-up, so that we could not examine SUA trajectories, and the fact that hyperuricemic HALT participants had farther advanced disease than those with normal SUA levels, indicated by larger kidneys, lower renal blood flow and lower eGFR at baseline. Strengths of this secondary analysis are the large trial size, a well-characterized study population with well controlled blood pressure using RAAS inhibitors, and long follow-up.
CONCLUSION
Among individuals with ADPKD who participated in the HALT PKD trials, those with high SUA levels were predominantly male and had higher BMI and lower baseline renal function than those with low SUA levels. In this population, SUA is not an independent predictor of disease progression as determined by kidney volume growth and renal function loss, nor does high SUA predict development of ESKD, death or 50% loss of eGFR independently from baseline renal function.
ACKNOWLEDGEMENTS:
List of HALT PKD centers and investigators: 1. University of Colorado Denver: Robert W. Schrier and Godela M. Brosnahan. 2. University of Pittsburgh: Kyongtae T. Bae, Charity G. Moore, Kaleab Z. Abebe. 3. Emory University: Arlene B. Chapman, Frederic F. Rahbari-Oskoui. 4. Mayo Clinic: Vicente E. Torres, Marie C. Hogan, Peter C. Harris. 5. Tufts Medical Center Boston: Ronald D. Perrone, Dana C. Miskulin. 6. Beth Israel Deaconess Medical Center Boston: Theodore I. Steinman. 7. Cleveland Clinic: William E. Braun. 8. University of Kansas Medical Center: Franz T. Winklhofer, Jared J. Grantham. 9. National Institutes of Health: Michael F. Flessner.
HALT PKD was supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (DK62402 to Dr. Schrier, DK62411 to Dr. Perrone, DK62410 to Dr. Torres, DK082230 to Dr. Moore, DK62408 to Dr. Chapman, and DK62401 to Washington University in St. Louis) and the National Center for Research Resources General Clinical Research Centers (RR000039 to Emory University, RR000585 to the Mayo Clinic, RR000054 to Tufts Medical Center, RR000051 to the University of Colorado, RR023940 to the University of Kansas Medical Center, and RR001032 to Beth Israel Deaconess Medical Center), National Center for Advancing Translational Sciences Clinical and Translational Science Awards (RR025008 and TR000454 to Emory University, RR024150 and TR00135 to the Mayo Clinic, RR025752 and TR001064 to Tufts University, RR025780 and TR001082 to the University of Colorado, RR025758 and TR001102 to Beth Israel Deaconess Medical Center, RR033179 and TR000001 to the University of Kansas Medical Center, and RR024989 and TR000439 to Cleveland Clinic), by funding from the Zell Family Foundation (to the University of Colorado), and by a grant from the PKD Foundation.
Mutation analysis was supported by DK62410-S1 to Dr. Harris and the Mayo Translational PKD Center (DK090728). Study drugs were donated by Boehringer Ingelheim Pharmaceuticals Inc (telmisartan and matched placebo) and Merck & Co Inc (lisinopril).
Most of all we thank the hundreds of patients who took part in the HALT-PKD trials and the dedicated study coordinators who guided them through the years of participation.
Conflict of Interest:
Dr. Brosnahan declares no conflict of interest. Dr. Chonchol has received grant funding from Otsuka, Sanofi and Kadmon. Dr. Gitomer, Dr. You and Dr. Wang declare no conflict of interest.
Footnotes
Consent for Publication:
All authors have read the paper and given consent for publication in its current form.
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