Background: To what extent uric acid (UA) levels and/or metabolic syndrome (Mets) contribute to the onset of chronic kidney disease (CKD) is largely unknown. The present study explores how these two factors have an association with the new incidence of CKD. Methods: Study design is a cohort study. A total of 14,485 participants were eligible for the cross-sectional analysis on UA levels and the prevalence of Mets. Among those individuals, 8,223 participants without CKD and 4,839 without Mets were eligible for the longitudinal analysis of the new incidence of CKD. Parameters monitored were body mass index, systolic and diastolic blood pressure, serum creatinine concentration, estimated glolerular filtration rate, lipid profiles, plasma glucose, HbA1c. The primary predictor was the level of UA and Mets to explain the newly-developed CKD. The observation period was 4 years. Results: In a cross-sectional analysis, higher UA levels were associated with the greater prevalence of Mets. In addition, UA levels were associated with the numbers of the Mets constituents in both genders. In a longitudinal analysis, higher UA levels were associated with the greater rate of CKD and the greater incidence of Mets. In addition, the incidence of CKD at year 4 was influenced by the presence of hyperuricemia, but not by that of the Mets. The odd ratio (OR) to predict the CKD incidence was 1.42 (95% confidence intervals (CI), 0.52 to 3.78) in the presence of Mets alone, 2.10 (95% CI, 1.36 to 3.23) in the presence of hyperuricemia alone, and 3.56 (95% CI, 1.55 to 8.21) in the presence of both. Conclusion: Hyperuricemia has a greater association with the incidence of CKD than Mets does. Hyperuricemia complicated by Mets is additionally detrimental.
Hyperuricemia plays a pivotal role in the progression of chronic kidney disease (CKD), which includes chronic glomerulonephritis [
In the last few decades, the metabolic syndrome (Mets) has been increasing in many countries. It is characterized by comorbid conditions such as hypertension, dyslipidemia and diabetes mellitus. Notablely, in the recent clinical guidelines for Mets, hyperuricemia has not been chosen as a constituent of the syndrome [
In the present trial, we used epidemiological cohorts in a screened population at large to conduct: 1) a cross- sectional analysis on the prevalence of Mets according to UA levels; and 2) a longitudinal survey to observe to what extent Mets and hyperuricemia contribute to the new onset of CKD. We applied strict exclusion criteria in order to choose eligible participants.
The study design is a retrospective population-based cohort of Japanese office workers aged 25 to 60 years (average 42 +/− 11), the majority of them were living in the vicinity of Tokyo district. The original number of the participants was 14,485 who had an annual medical check-up starting from 2009 to 2013 in our institutions.
The basic exclusion criteria are individuals receiving medications for diabetes, hyperuricemia, hypertension and dyslipidemia; those having insufficient clinical data; those with past history of incident MACE such as cerebral apoplexy or myocardial infarction; those with any disease requiring hospitalization; those with current cancer or other serious diseases judged by the physicians; and those with current pregnancy. These eliminations leave a total number of 14,485 subjects who are eligible for a cross-sectional analysis of the prevalence of Mets.
For a longitudinal analysis of the effect of either Mets or hyperuricemia, or both on the incidence of CKD, individuals with estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73m2 at entry were also excluded. The present and past history of proteinuria was also another rule out factor. The definition of CKD was a reduction of eGFR less than 60 mL/min/1.73m2 at any given time of the observation period of 4 years. This exclusion process finally leaves 8,223 CKD-free individuals for the analysis of the new onset of CKD.
A minim requirement of making diagnosis of Mets was defined as individuals with waist circumference ≥ 85 cm (if female 90 cm), with two or more additional abnormalities including hypertension (blood pressure (BP); ≥130/85 mmHg), fasting plasma glucose (FPG; ≥110 mg/dL), triglyceride (TG; ≥150 mg/dL) and/or HDL-cho- lesterol (HDLC; ≤40 mg/dL). Finally, this exclusion criterion leaves 4839 Mets-free individuals for the new onset of Mets in 4 years time. These subjects are all CKD-free at baseline.
The primary predictor was the level of UA and Mets to explain the newly-developed CKD. The new incidence of CKD was defined as a decline in eGFR to less than 60 mL/min/1.73m2 calculated at any given time of 4 observation years [
Body mass index (BMI) was calculated based on the equation; BMI = Body weight (BW) × 1/(Body Height) squared. Laboratory tests were carried out after an 8 to 12 hour fasting. Measurements were made on serum creatinine (Cr) concentration, serum uric acid (UA) concentration, blood urea nitrogen (BUN), electrolytes and lipid profiles including total cholesterol (TC), TG, HDLC, LDL-cholesterol (LDLC), hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG). Hyperuricemia was defined as UA ≥ 7 mg/dL [
The present study was conducted in accordance with “Recommendations on the Establishment of Animal Experimental Guidelines” approved at the 80th General Assembly of the Japanese Science Council in 1980, and the principles set out in the Declaration of Helsinki 1964 as modified by subsequent version revisions.
The present survey was submitted to the Institutional Review Board (IRB)/Ethics Committee of the Jikei University School of Medicine. After the deliberation the protocol was approved in 2014 by the ethics committee of the University with the clinical trial number 25 - 203 (7338).
The database and all statistical outputs were retained by the Jikei University. Statistical analyses were conducted at the University. The access to the database was limited as deemed necessary. The authors assume full responsibility for the completeness and accuracy of the content of the manuscript.
Cross-sectional analysis on the prevalence of Mets was performed as a function of UA levels by dividing the distribution into 4 UA groups. Cross-sectional associations of Mets and UA levels were performed by using Chi-square analysis for discrete variables, analysis of variance (ANOVA) for continuous variables. The prevalence of Mets in reference to different age groups (Age < 29 years old, 30 - 39, 40 - 49, 50 - 59, and 60<) was analyzed by Mann-Whitney test. For the analyses of Mets constituents and UA levels, after obtaining a statistical significance by ANOVA, Turkey’s test was applied to compare the randomly-chosen 2 pair groups.
In the longitudinal analysis, we examined the association of new incident CKD according to UA levels and the presence of Mets. For these outcomes, Cox proportional hazard analysis was used to estimate adjusted odds ratio (OR) and associated 95% CI. Due to the lack of statistical power in the female cohort, analysis was made on only the males in these longitudinal analyses. Variables significant in univariate analysis were considered to be potential confounders of UA in multivariate models.
Statistical analyses were carried out with Stat Flex version 6.0 (Artec Ltd. Co., Osaka, Japan). Data are presented as the mean +/− standard deviation (SD), unless otherwise indicated. P ≤ 0.05 is considered statistically significant. Confidence intervals (CI) are expressed as 95% CI.
After the exclusion at the start for the cross-sectional analysis, the study cohort had an average age of 42 +/− 11 years, an eGFR of 82.0 +/− 14.6 mL/min/1.73m2, an overall UA level of 5.7 +/− 1.4 mg/dL (
The prevalence of Mets in male gender was shown in
UA Groups (mg/dL) | ||||||
---|---|---|---|---|---|---|
Overall | <5.0 | 5.0 - 5.9 | 6.0 - 6.9 | ≥7.0 | ||
N | 14,485 | 4,096 | 4,060 | 3,809 | 2,520 | |
Age | 42 ± 11 | 40 ± 11 | 42 ± 12 | 43 ± 11 | 44 ± 10 | P < 0.001 |
Men (%) | 79.3 | 44.8 | 86.4 | 96.3 | 98.1 | |
BMI (Kg/m2) | 23 ± 4 | 22 ± 3 | 23 ± 3 | 24 ± 3 | 25 ± 4 | P < 0.001 |
Waist circumference (cm) | 81.1 ± 10.0 | 76.4 ± 9.3 | 80.4 ± 9.1 | 83.3 ± 9.3 | 86.8 ± 9.7 | P < 0.001 |
SBP (mmHg) | 119 ± 15 | 113 ± 15 | 119 ± 14 | 122 ± 14 | 124 ± 15 | P < 0.001 |
DBP (mmHg) | 75 ± 11 | 71 ± 11 | 75 ± 11 | 77 ± 11 | 80 ± 11 | P < 0.001 |
Cr (mg/dL) | 0.80 ± 0.14 | 0.70 ± 0.13 | 0.80 ± 0.12 | 0.84 ± 0.11 | 0.89 ± 0.14 | P < 0.001 |
eGFR (mL/min/1.73m2) | 83.0 ± 14.6 | 87.6 ± 15.0 | 83.7 ± 13.9 | 81.0 ± 13.6 | 77.2 ± 13.9 | P < 0.001 |
TC (mg/dL) | 199 ± 107 | 193 ± 31 | 197 ± 31 | 201 ± 32 | 210 ± 33 | P < 0.001 |
TG (mg/dL) | 107 ± 89 | 81 ± 56 | 100 ± 93 | 115 ± 79 | 147 ± 118 | P < 0.001 |
HDLC (mg/dL) | 61 ± 15 | 67 ± 15 | 61 ± 15 | 59 ± 15 | 57 ± 15 | P < 0.001 |
LDLC (mg/dL) | 119 ± 30 | 111 ± 28 | 118 ± 29 | 123 ± 30 | 129 ± 31 | P < 0.001 |
FPG (mg/dL) | 96 ± 18 | 94 ± 20 | 96 ± 17 | 97 ± 17 | 98 ± 15 | P < 0.001 |
HbA1c (NGSP) (%) | 5.43 ± 0.59 | 5.38 ± 0.64 | 5.42 ± 0.58 | 5.46 ± 0.57 | 5.50 ± 0.55 | P < 0.001 |
Mets: metabolic syndrome; UA: serum uric acid concentration; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; Cr: serum creatinine concentration; eGFR: estimated glomerular filtration rate; TC: total cholesterol; TG: triglycerides; HDLC: high-density lipoprotein cholesterol; LDLC: low-densitylipoprotein cholesterol; FPG: fasting plasma glucose concentration; HbA1c: hemoglobin A1c.
mg/dL), 13.9% (6.0 - 6.9 mg/dL) and 23.0% (≥7 mg/dL), respectively (by the Chi-square test, P < 0.001). In each UA group, there was a statistical difference in the prevalence according to age (P < 0.01, by Mann-Whitney test). This increasing trend of the Mets prevalence was prominent in the individual groups with older age.
cording to age (P < 0.01, by Mann-Whitney test. The increasing trend of the Mets prevalence was prominent in the individual groups with older age.
A total of 124 individuals had a decrease in eGFR less than 60 mL/min/1.73m2 at year 4 (124/6, 542 individuals = 2.0%). Among them, the rate of proteinuria appearance at year 4 was 1.6% (2/124 individuals). In 124 individuals with CKD at year 4, mean eGFR at baseline and closeout were 67.8 +/− 7.2 and 56.2 +/− 2.5 mL/min/1.73m2, respectively. In contrast, mean eGFR of individuals without CKD at baseline and closeout were 91.2 +/− 17.5 and 83.4 +/− 13.6 mL/min/1.73m2 (n = 6262), respectively.
After adjustment for age, sex, BMI, waist circumference, systolic BP (SBP), diastolic BP (DBP), TC, TG, HDLC, LDLC, FPG and HbA1c, the multivariate adjusted OR for new-onset of CKD (a decrease in eGFR < 60 mL/min/1.73m2 at year 4) in conjunction with hyperuricemia and Mets were calculated in
UA Groups (mg/dL) | ||||||
---|---|---|---|---|---|---|
Overall | <5.0 | 5.0 - 5.9 | 6.0 - 6.9 | ≥7.0 | ||
N | 8,223 | 2,631 | 2,418 | 2,118 | 1,056 | |
Age (y) | 39 ± 10 | 37 ± 9 | 39 ± 10 | 40 ± 10 | 41 ± 9 | <0.001 |
Men (%) | 77.7 | 42.2 | 89.6 | 97.9 | 98.8 | <0.001 |
Hypertension (%) | 7.7 | 3.8 | 7.1 | 10.6 | 15.1 | <0.001 |
BMI (Kg/m2) | 22 ± 3 | 21 ± 3 | 22 ± 3 | 23 ± 3 | 24 ± 3 | <0.001 |
Waist circumference (cm) | 78.6 ± 8.8 | 74.1 ± 8.2 | 78.6 ± 7.9 | 81.1 ± 7.9 | 84.2 ± 8.6 | <0.001 |
SBP (mmHg) | 118 ± 13 | 113 ± 12 | 118 ± 12 | 120 ± 13 | 123 ± 14 | <0.001 |
DBP (mmHg) | 74 ± 9 | 70 ± 9 | 74 ± 9 | 75 ± 9 | 77 ± 9 | <0.001 |
Cr (mg/dL) | 0.75 ± 0.14 | 0.65 ± 0.13 | 0.77 ± 0.11 | 0.80 ± 0.11 | 0.83 ± 0.11 | <0.001 |
eGFR (mL/min/1.73m2) | 91.2 ± 17.6 | 96.5 ± 19.5 | 91.1 ± 16.6 | 88.1 ± 15.4 | 84.3 ± 15.0 | <0.001 |
TC ( mg/dL) | 192 ± 29 | 187 ± 29 | 192 ± 29 | 194 ± 28 | 201 ± 28 | <0.001 |
TG (mg/dL) | 94 ± 59 | 72 ± 42 | 91 ± 51 | 106 ± 65 | 126 ± 76 | <0.001 |
HDLC (mg/dL) | 64 ± 15 | 69 ± 15 | 64 ± 15 | 61 ± 14 | 59 ± 16 | <0.001 |
LDLC (mg/dL) | 113 ± 26 | 106 ± 25 | 113 ± 26 | 117 ± 26 | 122 ± 27 | <0.001 |
FPG (mg/dL) | 93 ± 10 | 91 ± 9 | 93 ± 10 | 94 ± 10 | 96 ± 11 | <0.001 |
HbA1c (NGSP) (%) | 5.23 ± 0.34 | 5.21 ± 0.33 | 5.23 ± 0.33 | 5.25 ± 0.34 | 5.29 ± 0.37 | <0.001 |
CKD: chronic kidney disease; UA: serum uric acid concentration; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; Cr: serum creatinine concentration; eGFR: estimated glomerular filtration rate; TC: total cholesterol; TG: triglycerides; HDLC: high-density lipoprotein cholesterol; LDLC: low-densitylipoprotein cholesterol; FPG: fasting plasma glucose concentration; HbA1c: hemoglobin A1c.
both Mets and hyperuricemia. The association of hyperuricemia with the incidence of CKD appeared to be greater than that of the Mets. Moreover, the presence of both has a substantial additive effect on Mets or hyperuricemia alone.
The present study demonstrates that: 1) In a cross-sectional analysis, higher UA levels are associated with the
UA Groups (mg/dL) | ||||||
---|---|---|---|---|---|---|
Overall | <5.0 | 5.0 - 5.9 | 6.0 - 6.9 | ≥7.0 | ||
N | 4,839 | 1,440 | 1,446 | 1,297 | 656 | |
Age (y) | 41 ± 8 | 41 ± 8 | 42 ± 8 | 42 ± 7 | 42 ± 7 | <0.001 |
Men (%) | 81.3 | 48.1 | 91.1 | 98.5 | 98.6 | <0.001 |
BMI (Kg/m2) | 22 ± 3 | 21 ± 3 | 22 ± 3 | 23 ± 3 | 24 ± 3 | <0.001 |
Waist circumference (cm) | 79.0 ± 8.3 | 75.1 ± 8.0 | 78.9 ± 7.7 | 81.0 ± 7.4 | 83.6 ± 7.8 | <0.001 |
SBP (mmHg) | 117 ± 13 | 113 ± 12 | 117 ± 12 | 119 ± 13 | 121 ± 13 | <0.001 |
DBP (mmHg) | 74 ± 9 | 71 ± 9 | 74 ± 9 | 75 ± 9 | 76 ± 9 | <0.001 |
Cr (mg/dL) | 0.76 ± 0.13 | 0.68 ± 0.13 | 0.78 ± 0.11 | 0.81 ± 0.11 | 0.83 ± 0.11 | <0.001 |
eGFR (mL/min/1.73m2) | 87.6 ± 14.9 | 91.2 ± 16.1 | 87.9 ± 14.5 | 85.8 ± 13.7 | 82.8 ± 13.1 | <0.001 |
TC ( mg/dL) | 194 ± 29 | 190 ± 29 | 194 ± 29 | 196 ± 28 | 202 ± 29 | <0.001 |
TG (mg/dL) | 95 ± 59 | 75 ± 44 | 93 ± 50 | 106 ± 64 | 123 ± 77 | <0.001 |
HDLC (mg/dL) | 64 ± 16 | 69 ± 16 | 64 ± 15 | 61 ± 14 | 60 ± 16 | <0.001 |
LDLC (mg/dL) | 115 ± 26 | 109 ± 25 | 115 ± 26 | 118 ± 25 | 123 ± 27 | <0.001 |
FPG (mg/dL) | 94 ± 9 | 92 ± 9 | 94 ± 9 | 95 ± 9 | 96 ± 10 | <0.001 |
HbA1c (NGSP) (%) | 5.26 ± 0.3 | 5.24 ± 0.30 | 5.25 ± 0.30 | 5.27 ± 0.29 | 5.28 ± 0.33 | <0.01 |
Mets: metabolic syndrome; UA: serum uric acid concentration; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; Cr: serum creatinine concentration; eGFR: estimated glomerular filtration rate; TC: total cholesterol; TG: triglycerides; HDLC: high-density lipoprotein cholesterol; LDLC: low-densitylipoprotein cholesterol; FPG: fasting plasma glucose concentration; HbA1c: hemoglobin A1c.
greater prevalence of CKD and Mets; and 2) In a longitudinal analysis, hyperuricemia and Mets are associated with the new incidence of CKD in which the association of the former appears to be substantially greater than that of the latter.
The strengths of this study are the large number of individuals who did not have CKD at the entry, the complete nature of the data set, and the ability to link demographic and clinical factors with CKD. Other advantages include the ability to adjust for multiple factors that may affect initiation of CKD. We must also emphasize that
we controlled for the currently treated diabetes, hypertension, hyperuricemia, dyslipidemia and MACE etc., all known as well-established predictors of CKD.
To date, a large body of epidemiological trials demonstrates that increased UA is associated with increased risk for future hypertension, diabetic nephropathy and CKD, and that hyperuricemia, per se, could be an independent risk factor for poor cardiovascular prognosis [
Previous reports showed that hyperuricemia is closely related with visceral fat volume and that changes in UA levels are also strongly correlated with changes in the visceral fat volume [
Advanced CKD is a high risk condition, since it frequently induces fatal cardiovascular complications such as MACE [
Choi et al. reported that the prevalence of the Mets increases substantially with increasing levels of serum UA, suggesting that physicians should recognize the Mets as a frequent comorbidity of hyperuricemia and the treatment of hyperuricemia to prevent serious complications could be crucial [
The association of serum UA and Mets with CKD remains controversial. See and associates using data from 81,799 adults with or without Mets (45,148 men and 36,651 women) suggested that hyperuricemia is strongly associated with CKD, independent of the presence of Mets [
Although it is beyond the scope of the present study, presuming the mechanism of UA-induced kidney dysfunction is of great interest. Although the entire pathogenesis is largely unknown, the mechanisms by which UA exerts a BP raising effect have been described as a UA-mediated hypertension, which can be highlighted that increase in circulating UA induces vasoconstrictive-hypertension in the early stage and salt sensitive volume- expanded hypertension in the late stage. This is, presumably, via an activation of intra renal renin-angiotensin system, an increased reactive oxygen species, and a decreased nitric oxide production [
Despite the large scale of the data setting, the present study has several limitations. First, the definition of CKD incidence is merely based on changes in eGFR, rather than more precise measures of endogenous Cr clearane. Second, this is a retrospective study in which “Cause & Effect” is not always appropriately proven. Third, we studied a relatively younger healthy population, which may make it difficult to extrapolate the results to other ill populations. Fourth, residual confounding, by unmeasured factors or by kidney function, could have been present in this analysis. Finally, we did not have information on urine protein excretion, which could have either strengthened or attenuated the results.
The present study shows that both hyperuricemia and Mets have an impact on the new incidence of CKD. The former appears to be more influential than the latter.
A part of this study was presented at the annual scientific meeting of the Japanese Society of Hypertension held in Ehime Japn in 11 Ocober 2015.
The authors have declared that no conflict of interest exists.
Satoru Kuriyama,Shinichiro Nishio,Satoshi Kidoguchi,Kosuke Honda,Yasuhito Takahashi,Naoki Sugano,Yukio Maruyama,Tatsuo Hosoya,Tomoko Nakano,Tomoko Tanabe,Edward Stim,Takashi Yokoo, (2016) A Greater Association of Hyperuricemia than of Metabolic Syndrome with the New Incidence of Chronic Kidney Disease. Open Journal of Nephrology,06,17-27. doi: 10.4236/ojneph.2016.61003