Uric acid is the end product of purine metabolism in humans, and increased serum uric acid concentrations lead to gout. The objective of the current study was to identify factors that are independently associated with serum uric acid concentrations in a cohort of Czech control individuals.
The cohort consisted of 589 healthy subjects aged 18–65 years. We studied the associations between the serum uric acid concentration and the following: (i) demographic, anthropometric and other variables previously reported to be associated with serum uric acid concentrations; (ii) the presence of metabolic syndrome and the levels of metabolic syndrome components; and (iii) selected genetic variants of the MTHFR (c.665C>T, c.1286A>C), SLC2A9 (c.844G>A, c.881G>A) and ABCG2 genes (c.421C>A). A backward model selection procedure was used to build two multiple linear regression models; in the second model, the number of metabolic syndrome criteria that were met replaced the metabolic syndrome-related variables.
The models had coefficients of determination of 0.59 and 0.53. The serum uric acid concentration strongly correlated with conventional determinants including male sex, and with metabolic syndrome-related variables. In the simplified second model, the serum uric acid concentration positively correlated with the number of metabolic syndrome criteria that were met, and this model retained the explanatory power of the first model. Moderate wine drinking did not increase serum uric acid concentrations, and the urate transporter ABCG2, unlike MTHFR, was a genetic determinant of serum uric acid concentrations.
Citation: Stibůrková B, Pavlíková M, Sokolová J, Kožich V (2014) Metabolic Syndrome, Alcohol Consumption and Genetic Factors Are Associated with Serum Uric Acid Concentration. PLoS ONE 9(5): e97646. https://doi.org/10.1371/journal.pone.0097646
Editor: Pedro A. Jose, University of Maryland School of Medicine, United States of America
Received: February 6, 2014; Accepted: April 22, 2014; Published: May 14, 2014
Copyright: © 2014 Stibůrková 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.
Funding: This study was supported by a grant from the Czech Republic Ministry of Health (IGA MZ NT/11322–4/2010 and RVO VFN64165) and by institutional support from a program at Charles University in Prague (PRVOUK-P24/LF1/3). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
An increased serum uric acid (UA) concentration (>416 µmol/L in men and >340 µmol/L in children and women) is an important risk factor for gout and has other significant associations with human disorders, such as cardiovascular and renal diseases , . Links between increased serum UA concentrations and the levels of the individual components of metabolic syndrome (MS) have been reported , . Serum UA concentrations have also been linked to many ageing-related illnesses and to brain function, although the direction of the association is unclear. It has been suggested that higher UA concentrations may be associated with increased performance on memory-related tasks . Studies have provided evidence that UA and gout lower the risk of developing Parkinson's disease , . Many factors, including obesity, body mass index and alcohol consumption, are known to influence serum UA concentrations , –. In addition, data from numerous studies suggest that serum UA concentrations are markedly heritable; the proportion of variance explained by a shared genetic background ranges from 0.38–0.63 –.
UA is the end product of purine metabolism in humans and higher primates; in contrast, other mammals further metabolize urate to allantoin. The absence of the hepatic enzyme uricase (urate oxidase) and efficient renal UA reabsorption contributes to the ten-fold higher UA blood levels in humans compared to those of other mammals. UA is a powerful scavenger of peroxyl radicals, hydroxyl radicals and singlet oxygen in human biological fluids. The balance between UA production and excretion determines serum UA concentrations; the daily production of urate is approximately 1000 mg (6 mmol) in adults. Approximately 75% of UA excretion occurs via the kidney, and the gastrointestinal tract eliminates 25%. The renal excretion of UA depends on the balance between filtration, secretion and reabsorption. Transporters for urate localize in the proximal tubule, where specific transporter proteins in the apical and basolateral membranes secrete and extensively reabsorb urate. As a result, approximately 10% of the UA filtered through the glomerular membranes is excreted.
Many studies have shown an association between alcohol consumption and hyperuricemia, which is a common precipitating cause of acute gouty arthritis , . Alcohol consumption is believed to elevate serum UA through two mechanisms: the accelerated synthesis of UA from adenosine, which is produced after the degradation of adenosine triphosphate to adenosine monophosphate, and reduced urinary excretion due to the elevation of blood lactate caused by the oxidation of ethanol .
Over the past decade, genome-wide association studies and meta-analyses have led to a massive increase in our knowledge of the common genetic variants that influence serum UA concentrations. However, detailed knowledge of the degree to which genetic variants predict serum UA concentrations and of the functional pathway through which these variants may alter serum UA remains limited. The genes that influence the level of serum UA via renal UA excretion are those for the urate transporters, primarily those encoded by the SLC2A9, SLC22A12 and ABCG2 genes. The URAT1 transporter, the product of SLC22A12, was identified in 2002 as the crucial transporter involved in UA reabsorption. The URAT1 transporter regulates blood UA levels and plays a central role in the reabsorption of UA from the glomerular filtrate . Loss-of-function mutations in the SLC22A12 gene cause renal hypouricemia type 1, OMIM #220150 , . In 2008, the primary role of SLC2A9 in UA reabsorption was identified , . Recent results revealed significant correlations between genetic variants in SLC2A9 and serum UA levels, the excreted fraction of UA, blood pressure, body mass index and gout –. The SLC2A9 genetic variants are responsible for a portion of the variance in serum UA concentrations: 5–6% in females and 1–2% in males –. Mutations in the SLC2A9 gene cause renal hypouricemia type 2, OMIM #612076 –. UA secretion is mostly determined by the transporter ATP-binding cassette subfamily G member 2 (ABCG2/BCRP). Variant c.421C>A (rs2231142, p.Q141K) results in a reduction of the urate transport rate by 53% compared with that for the wild-type ABCG2 and causes approximately 10% of the hyperuricemia and gout cases in Caucasians .
Although most genes associated with UA are involved in the renal urate transport system, an association between homozygosity for the common genetic variant c.665C>T (known as c.677C>T; rs1801133, p.A222V) of methylenetetrahydrofolate reductase (MTHFR, 1p36.22, EC 18.104.22.168) and the serum UA concentration was also reported –. MTHFR is a cytoplasmic enzyme that catalyzes the reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, the primary methyl donor for the methylation of homocysteine to methionine. It has been speculated that MTHFR allelic variants may be confounding genetic factors in the relationship between serum UA and hypertension .
Even with extensive research via different approaches into the basis of serum UA concentrations, a complete understanding of serum UA production, excretion and interference has not yet been reached. Our study was aimed at answering the following unresolved questions: (a) What is the relationship between serum UA and demographic, anthropometric and other variables? (b) What is the relationship between serum UA and metabolic syndrome? (c) What is the influence of genetic determinants on serum UA? In our study, we examined the associations between serum UA concentrations and a wide range of variables in a cohort from central Europe. We modeled the associations between UA and (i) demographic, anthropometric and other variables previously reported to be associated with serum UA (referred to as conventional variables); (ii) the presence of metabolic syndrome (MS) and the levels of MS components and (iii) selected genetic variants in the SLC2A9, ABCG2 and MTHFR genes. We have adopted a complex approach, combining all these variables into one regression model.
Materials and Methods
We have used the data and genomic DNA samples from subjects of a previously described study . All participants were 18–65 years old, residents of Central Bohemia or Prague with no history and signs of coronary artery disease, peripheral arterial disease and stroke. They were recruited from health clinics, general practitioner's offices, on community health days and by advertising in company bulletins. This study collected various anamnestic, clinical and biochemical parameters and questionnaire-based data about lifestyle, e.g., smoking, the consumption of alcoholic beverages, the consumption of fruit and vegetables, caloric restriction and vitamin use. The original cohort consisted of 591 subjects. Two subjects did not have recorded UA concentrations and were excluded from the analysis, resulting in a total of 589 subjects. Their characteristics are summarized in Table 1. The median UA concentrations were 330 µmol/L in men (n = 285) and 232 µmol/L in women (n = 304). The UA concentrations of 536 subjects fell within the normal UA concentration range. There were 3 subjects (all women) with UA concentrations below the reference range and 50 subjects (35 men and 15 women) with UA concentrations above the reference range and all remained in the study. The study was approved by the Ethics Committee of Charles University – First Faculty of Medicine; all subjects gave their informed written consent.
Clinical and biochemical investigations
UA in the serum was measured using a uricase enzymatic method by an auto-analyzer. We studied the associations between the serum UA concentration and the following demographic characteristics, metabolite levels, conventional risk factors and genetic risk factors: (i) conventional variables—sex, age, alcohol consumption (beer and wine), smoking status and number of packyears, allopurinol use, diuretic use, acetylsalicylic acid consumption, hormonal treatment, hormonal contraception and serum creatinine levels, (ii) metabolic syndrome-related variables—BMI, WHR (waist-to-hip ratio), systolic and diastolic pressure, hypertension, treatment of hypertension, hyperglycemia, diabetes mellitus and its treatment, total cholesterol, HDL and LDL cholesterol, triacylglycerol levels, hyperlipidemia and the gamma-glutamyltransferase (GGT) concentration and (iii) selected genetic variants (see above). For the second analysis, we constructed a new variable called metabolic syndrome (the number of metabolic criteria met) and used it instead of the MS-related variables. We used the following common criteria for MS reported by several organizations, including the WHO, the IDF and the AHA/NHLBI : abdominal obesity, elevated triglycerides (≥1.7 mmol/L), reduced HDL-cholesterol (<1.00 mmol/L in males and <1.3 mmol/L in females), elevated blood pressure (systolic pressure ≥130, diastolic pressure ≥85 mmHg or treatment for hypertension) and elevated fasting glucose concentration (≥5.6 mmol/L and/or treated for diabetes mellitus). Because waist circumference data were not available, we instead used the criteria of a BMI ≥25 kg/m2 and/or a WHR ≥0.90 for men and ≥0.85 for women  as indicators of obesity.
The frequent genetic variants of genes encoding the urate transporter SLC2A9 (c.844G>A (rs16890979, p.V282I) and c.881G>A (rs3733591, p.R294H)), the urate transporter ABCG2 (c.421C>A (rs2231142, p.Q141K)) and the MTHFR enzyme (c.665C>T (known as c.677C>T; rs1801133, p.A222V) and c.1286A>C (known as c.1298A>C; rs1801131, p.E429A)) were selected for genotype analysis. The selected genetic variants of the urate transporters were analyzed by PCR with allele-specific primers (ARMS-PCR); the primers and conditions are given in Table S1. The MTHFR variants were determined in a previous study .
In total, 30 potential predictors were included in the analysis. For statistical purposes, the UA concentration and the glucose, GGT and triglyceride levels were logarithmically transformed. The association between the variables and the serum UA concentration was evaluated in two steps. First, single-predictor linear regression models were utilized with the UA concentration as the response variable and one predictor variable adjusted for sex (including possible interactions with sex). Then, the backward model selection procedure was used to build a multiple linear regression model. This model was based on the set of variables that showed association (p-value <0.1) in the single-predictor models, together with all interaction terms with sex. In the second multiple linear regression analysis, we started with the same large set of variables in interaction with sex, but we replaced the predictor variables related to metabolic syndrome with the number of MS criteria met and repeated the backward selection procedure.
We assessed the relative effect of the genetic component in the final regression models by analyzing the proportion of the explained variance relative to the adjusted R2 value. The logarithmic transformation of the UA concentration made it difficult to interpret the values of the coefficients in the multiple regression models. We therefore calculated the model-predicted mean change in UA concentration when the studied variable increased by one unit while the values of the other variables remained the same for an individual whose original UA concentration was 326 µmol/L (the mean UA concentration in men; the UA concentration in women was calculated from the model).
The level of statistical significance was set to 0.05 for the coefficients in the multiple regression analysis models. All analyses were performed in the statistical language and environment R, version 2.14.0 (http://www.R-project.org).
Single-predictor regression analysis
Table S2 shows the association of individual variables with the UA concentration in the single-predictor regression analysis (with sex as the covariate). In our model the majority of conventional and MS-related variables were associated with the UA concentration indicating a high reliability of data.
Genetic factors were significantly associated with the serum UA concentrations. The allelic and genotype frequencies and geometric means of serum UA concentrations in wild-type homozygotes, heterozygotes and variant homozygotes for different loci are shown in Table 2. All genotypes were in Hardy-Weinberg equilibrium with the exception of the MTHFR variant c.665C>T in males (p<0.022); since the genotyping errors are unlikely this observation may be due to the population stratification or by random occurrence. There was a significant additive effect of ABCG2 c.421C>A (p-value <0.001) on the increase in the UA concentration. The single-predictor analysis of the effect of the SLC2A9 c.881G>A variant showed a possible additive effect on the decrease in the serum UA concentration (p-value <0.001), whereas the SLC2A9 c.844G>A variant did not show any effect on the UA concentration. No significant relationship was found between the MTHFR variant c.665C>T and the UA concentration. Homozygous individuals with the MTHFR c.1286A>C variant had significantly lower UA concentrations (p-value = 0.025) than those of the remainder of the study population. However, when MTHFR c.1286A>C was considered as a three-level factor (A/A, A/C and C/C genotypes), the model p-value was greater than the significance level (p-value = 0.092).
In the study population, 30% of the subjects had MS (106 men and 70 women met three or more MS criteria), and 70% of the subjects did not have MS (178 men and 233 women). The box and whisker plot in Figure 1 shows the distribution of the serum UA concentrations based on the number of MS criteria met by the study subjects. Although the figure is not adjusted for other potentially influential factors, it suggests the tendency for the serum UA concentration to increase as more MS criteria were met.
The boxes show median (black bar), quartiles (box limits) and extreme values of uric acid concentrations for individuals with 0, 1, 2, 3, 4 or 5 metabolic syndrome criteria met. The line shows estimated geometric mean of the uric acid concentrations, as calculated from Model 2. The band shows 95% confidence limit for the estimate.
Multiple regression analysis
To assess the independent contributions of the variables to the UA concentration, a multiple linear regression model was built (Model 1 in Table 3 and Table S2). The variability in the serum UA concentrations was best explained by sex; age; BMI; hypertension; hyperglycemia; the serum levels of triacylglycerols, creatinine and GGT; allopurinol intake, MTHFR c.1286A>C, ABCG2 c.421C>A, SLC2A9 c.844G>A and SLC2A9 c.881G>A. The adjusted coefficient of determination (R2) was 0.59. The genetic variables together explained approximately 4.5% of the total variability.
The final model contained five variables associated with MS. Four were associated with an increase in the serum UA concentration: BMI, hypertension, triacylglycerol levels and GGT levels. Hyperglycemia was associated with a slight decrease in serum UA concentration. An alternative multiple linear regression model was constructed in which all variables associated with MS were replaced by the number of MS criteria that were met, and the backwards selection model procedure was re-run. The alternative model is shown as Model 2 in Table 3 and in Table S2. The coefficient of determination was 0.53, which was very similar to Model 1. The genetic variables explained approximately 4% of the total variability in the simplified Model 2. The linear effect of MS was highly significant; this model suggests that there is an important association of MS with the serum UA concentration. Figure 1 displays the model-predicted mean values of UA and the 95% confidence intervals in men and women for the various numbers of MS criteria that were met while the other variables remained the same. In addition to MS and genetic variables, the serum UA concentration was positively associated with menopause, beer consumption (Model 2 only), the serum creatinine level, the use of allopurinol and the use of diuretics (Model 2 only), whereas it was negatively associated with female sex, age and wine consumption (Model 2 only).
In both models, the serum UA concentration was higher for the individuals with the C allele of the ABCG2 c.421C>A gene. The increase in UA as the number of mutant alleles increased was five times higher for men than for women (Model 1; in Model 2, the interaction with sex was not statistically significant). The SLC2A9 c.881G>A and c.844G>A variants showed negative effects on the serum UA concentrations. In both models, the serum UA concentration was lower for individuals with the MTHFR c.1286CC genotype.
In the present study, we demonstrated the following new findings: (a) the serum UA concentration was positively correlated with the number of MS criteria that were met, and moreover, this simplification had no substantial influence on the explanatory power of the model; (b) moderate wine drinking likely does not increase serum UA concentrations; and (c) the urate transporter ABCG2, unlike MTHFR, is one of the genetic determinants of serum UA concentrations. The evidence for an influence of two SLC2A9 gene variants is inconclusive.
We showed that the serum UA concentration correlates positively (p<0.001) with the number of MS criteria (linear Model 2, Figure 1). Furthermore, we showed that this simplification has no substantial influence on the explanatory power of the model (the adjusted R2 of the simplified model is 0.53, and that of the full model is 0.59). Several studies explored the links between increased serum UA concentration and various components of MS . Some studies reported that the association between UA and MS components was stronger in females than in males . This result was not found in our study. Two recent studies ,  explored the association between higher UA levels and future MS development in two Asian populations. The design of our study cannot resolve the direction of causality; however, our findings, together with those mentioned above, may have implications for the use of serum UA concentration as a risk marker of metabolic syndrome and vice versa and for screening for hyperuricemia-related problems in patients with MS in clinical practice.
Alcohol consumption in our study was analyzed separately for beer and for wine. Although the serum UA concentration increased with increasing beer intake, our results supported the previously reported solitary finding that moderate wine drinking does not increase the serum UA concentration . Beer is an alcoholic beverage with high purine content, whereas wine contains a minimal amount of purines and several nonalcoholic components, including antioxidants, vasorelaxants and stimulants of anti-aggregatory mechanisms. Thus, the effect of the purines ingested from beer on the serum UA concentrations may be sufficient to augment the hyperuricemic effect of alcohol itself; however, the absence of an effect of wine may result from non-alcoholic components with antioxidant properties, such as polyphenols, which may mitigate the impact of alcohol on the serum UA concentration.
We found no significant relationship between the MTHFR c.665C>T variant and the UA concentration, and our finding is supported by two studies in Japanese subjects , . However, previously reported studies demonstrated a significant association between the serum UA concentration and the MTHFR c.665C>T allele in other ethnic groups –. The relationship between MTHFR c.1286A>C and serum UA concentrations has not been studied. The mechanism that determines the relationship between MTHFR polymorphisms and serum UA concentrations is unknown. Some studies presumed that the MTHFR allelic variants affected de novo purine synthesis via 10-formyl tetrahydrofolate, with the consequent overproduction of UA by the substrate of the MTHFR reaction , . Our finding that people with the MTHFR c.1286C homozygous genotype had significantly decreased UA concentrations should be considered as a topic for further exploration.
Most of the genes that influence the serum UA concentration are involved in renal excretion. Recent genome-wide association studies have identified a relationship between common genetic variants of urate transporters and either increased serum UA concentrations or primary gout. The most significant SNPs were c.421C>A in the secretion transporter ABCG2  and c.844G>A and c.881G>A in the urate reabsorption transporter SLC2A9 –.
Our study showed a strong association between the allelic variant c.421C>A in the ABCG2 transporter and increasing serum UA concentrations. This effect was more pronounced in males than in females (Model 1) and was independent of all other covariates. Our results confirm that this alteration in the ABCG2 gene is a common cause of hyperuricemia due to decreased renal urate excretion. This finding is consistent with previously reported studies in which the ABCG2 c.421C>A variant , , has been associated with hyperuricemia and gout in individuals of European, Han Chinese, Japanese and African-American ancestry , , , , however, this association was not found in Maori subjects . In contrast, we found that the SLC2A9 c.844G>A and c.881G>A variants were associated with a significant decrease in serum UA concentrations. This result is surprising, as all other studies reported increased serum UA levels or mixed results in individuals carrying c.844G>A and c.881G>A , . More precisely, the c.844G>A variant in SLC2A9 was associated with elevated serum uric acid concentration (especially in women) in the Framingham and Rotterdam cohorts , and in the island population of the Adriatic coast of Croatia  but not in African-Americans . Variant c.881G>A in the SLC2A9 gene was significantly associated with elevated serum uric acid concentrations and gout in the Han Chinese, Solomon Island and Japanese cohorts , , but not in the Eastern Polynesians, Western Polynesians and Europeans , . Since the genotype frequencies in our study were similar to other studies (with the exception of the Adriatic cohort ) it may be hypothesized that additional factors including dietary and life-style habits, and the absence of linkage disequilibrium play role in negative association of the SLC2A9 variants with serum UA concentrations in the Czech cohort.
Using a multiple linear regression model, we confirmed the previously reported sex-specific differences and the strong associations between serum UA concentration and age, menopause, BMI, hypertension, alcohol consumption, serum triacylglycerol levels, creatinine levels and GGT levels , –, –. Our study also examined additional determinants of serum UA concentrations, such as MS, type of alcohol consumption and five selected genetic variants. These associations have been also studied previously, however, these variables were not analyzed simultaneously in a multiple regression model.
Our study has several strengths. First, we explored the association between serum UA concentrations and several important demographic, anamnestic, biochemical and genetic factors in a Central European (Czech) population with detailed characterization that allowed us to answer our research question and that could be generalized to the larger population. Second, we controlled for several potential confounders, such as the types of medications, that may influence serum uric acid concentrations. Third, we used a complex approach, a multiple linear regression model that provides a comprehensive estimate of the association. Different limitations of this study should also be acknowledged. First, the size of the cohort may not be sufficiently large for such detailed analysis. Second, the number of the frequent genetic variants of genes encoding the urate transporters was limited.
In conclusion, we showed that the serum UA concentration positively correlated with the number of MS criteria that were met. We found that moderate wine drinking may not increase serum UA concentrations. We confirmed that the urate transporter ABCG2 is one of the genetic determinants of serum UA concentrations. These data may have implications for the clinical prediction of UA concentrations and may lead to personalized medical diagnoses and treatments for common diseases. An increased serum UA concentration is significantly associated with MS. In clinical practice, hyperuricemia is an indication for investigating MS criteria, and the presence of MS is an indication for investigating the serum UA concentration.
Genotype analysis - arrangement and primers. The selected genotypes were determined using multiplex ARMS-PCR. The amplifications were performed in the total volume 10 µl using Combi PPP master mix (Top-Bio s.r.o., Prague, Czech Republic) supplied with allele specific primer pairs as it is shown in this table. The cycling conditions were [95/2 min;30 x (95/10 s, 68/30 s); 68/5 min] in DNA Engine Dyad PTC-220 (MJ Research, Waltham, Massachusetts). PCR products were analysed on 3% agarose gels.
The final multiple linear regression model (Model 1) and the alternative model, with MS predictors replaced by graded MS (Model 2). * log-transformed for the model, coefficient and standard error relevant for the transformed variable. ** values for man/woman because of an interaction with gender present in the model.
The authors thank Dr.Ivan Šebesta for the initial discussion of the study and Michaela Šedová, MSc. for regression model ideas.
Conceived and designed the experiments: BS MP VK. Performed the experiments: BS JS. Analyzed the data: VK BS MP JS. Contributed reagents/materials/analysis tools: BS VK JS. Wrote the paper: BS VK MP.
- 1. Hayden MR, Tyagi SC (2004) Uric acid: A new look at an old risk marker for cardiovascular disease, metabolic syndrome, and type 2 diabetes mellitus: The urate redox shuttle. Nutr Metab (Lond) 1: 10.
- 2. Heinig M, Johnson RJ (2006) Role of uric acid in hypertension, renal disease, and metabolic syndrome. Cleve Clin J Med 73: 1059–1064.
- 3. Onat A, Uyarel H, Hergenc G, Karabulut A, Albayrak S, et al. (2006) Serum uric acid is a determinant of metabolic syndrome in a population-based study. Am J Hypertens 19: 1055–1062.
- 4. Yang T, Chu CH, Bai CH, You SL, Chou YC, et al. (2012) Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study. Atherosclerosis 220: 525–531.
- 5. Houlihan LM, Wyatt ND, Harris SE, Hayward C, Gow AJ, et al. (2010) Variation in the uric acid transporter gene (SLC2A9) and memory performance. Hum Mol Genet 19: 2321–2330.
- 6. Davis JW, Grandinetti A, Waslien CI, Ross GW, White LR, et al. (1996) Observations on serum uric acid levels and the risk of idiopathic Parkinson's disease. Am J Epidemiol 144: 480–484.
- 7. De Vera M, Rahman MM, Rankin J, Kopec J, Gao X, et al. (2008) Gout and the risk of Parkinson's disease: a cohort study. Arthritis Rheum 59: 1549–1554.
- 8. Bengtsson C, Tibblin E (1974) Serum uric acid levels in women. An epidemiological survey with special reference to women with high serum uric acid values. Acta Med Scand 196: 93–102.
- 9. Wingrove CS, Walton C, Stevenson JC (1998) The effect of menopause on serum uric acid levels in non-obese healthy women. Metabolism 47: 435–438.
- 10. Rathmann W, Funkhouser E, Dyer AR, Roseman JM (1998) Relations of hyperuricemia with the various components of the insulin resistance syndrome in young black and white adults: the CARDIA study. Coronary Artery Risk Development in Young Adults. Ann Epidemiol 8: 250–261.
- 11. Meisinger C, Doring A, Stockl D, Thorand B, Kowall B, et al. (2012) Uric acid is more strongly associated with impaired glucose regulation in women than in men from the general population: the KORA F4-Study. PLoS One 7: e37180.
- 12. Tang W, Miller MB, Rich SS, North KE, Pankow JS, et al. (2003) Linkage analysis of a composite factor for the multiple metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study. Diabetes 52: 2840–2847.
- 13. Wilk JB, Djousse L, Borecki I, Atwood LD, Hunt SC, et al. (2000) Segregation analysis of serum uric acid in the NHLBI Family Heart Study. Hum Genet 106: 355–359.
- 14. Nath SD, Voruganti VS, Arar NH, Thameem F, Lopez-Alvarenga JC, et al. (2007) Genome scan for determinants of serum uric acid variability. J Am Soc Nephrol 18: 3156–3163.
- 15. Yang Q, Guo CY, Cupples LA, Levy D, Wilson PW, et al. (2005) Genome-wide search for genes affecting serum uric acid levels: the Framingham Heart Study. Metabolism 54: 1435–1441.
- 16. Faller J, Fox IH (1982) Ethanol-induced hyperuricemia: evidence for increased urate production by activation of adenine nucleotide turnover. N Engl J Med 307: 1598–1602.
- 17. Puig JG, Fox IH (1984) Ethanol-induced activation of adenine nucleotide turnover. Evidence for a role of acetate. J Clin Invest 74: 936–941.
- 18. Nakamura K, Sakurai M, Miura K, Morikawa Y, Yoshita K, et al. (2012) Alcohol intake and the risk of hyperuricaemia: a 6-year prospective study in Japanese men. Nutr Metab Cardiovasc Dis 22: 989–996.
- 19. Enomoto A, Kimura H, Chairoungdua A, Shigeta Y, Jutabha P, et al. (2002) Molecular identification of a renal urate anion exchanger that regulates blood urate levels. Nature 417: 447–452.
- 20. Stiburkova B, Sebesta I, Ichida K, Nakamura M, Hulkova H, et al. (2013) Novel allelic variants and evidence for a prevalent mutation in URAT1 causing renal hypouricemia: biochemical, genetics and functional analysis. Eur J Hum Genet 21: 1067–1073.
- 21. Ichida K, Hosoyamada M, Hisatome I, Enomoto A, Hikita M, et al. (2004) Clinical and molecular analysis of patients with renal hypouricemia in Japan-influence of URAT1 gene on urinary urate excretion. J Am Soc Nephrol 15: 164–173.
- 22. Doring A, Gieger C, Mehta D, Gohlke H, Prokisch H, et al. (2008) SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat Genet 40: 430–436.
- 23. Vitart V, Rudan I, Hayward C, Gray NK, Floyd J, et al. (2008) SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout. Nat Genet 40: 437–442.
- 24. Caulfield MJ, Munroe PB, O'Neill D, Witkowska K, Charchar FJ, et al. (2008) SLC2A9 is a high-capacity urate transporter in humans. PLoS Med 5: e197.
- 25. Matsuo H, Chiba T, Nagamori S, Nakayama A, Domoto H, et al. (2008) Mutations in glucose transporter 9 gene SLC2A9 cause renal hypouricemia. Am J Hum Genet 83: 744–751.
- 26. Stiburkova B, Ichida K, Sebesta I (2011) Novel homozygous insertion in SLC2A9 gene caused renal hypouricemia. Mol Genet Metab 102: 430–435.
- 27. Stiburkova B, Taylor J, Marinaki AM, Sebesta I (2012) Acute kidney injury in two children caused by renal hypouricaemia type 2. Pediatr Nephrol 27: 1411–1415.
- 28. Woodward OM, Kottgen A, Coresh J, Boerwinkle E, Guggino WB, et al. (2009) Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc Natl Acad Sci U S A 106: 10338–10342.
- 29. Golbahar J, Aminzadeh MA, Al-Shboul QM, Kassab S, Rezaian GR (2007) Association of methylenetetrahydrofolate reductase (C677T) polymorphism with hyperuricemia. Nutr Metab Cardiovasc Dis 17: 462–467.
- 30. Hong YS, Lee MJ, Kim KH, Lee SH, Lee YH, et al. (2004) The C677 mutation in methylene tetrahydrofolate reductase gene: correlation with uric acid and cardiovascular risk factors in elderly Korean men. J Korean Med Sci 19: 209–213.
- 31. Itou S, Goto Y, Suzuki K, Kawai S, Naito M, et al. (2009) Significant association between methylenetetrahydrofolate reductase 677T allele and hyperuricemia among adult Japanese subjects. Nutr Res 29: 710–715.
- 32. Zuo M, Nishio H, Lee MJ, Maejima K, Mimura S, et al. (2000) The C677T mutation in the methylene tetrahydrofolate reductase gene increases serum uric acid in elderly men. J Hum Genet 45: 257–262.
- 33. Motti C, Gnasso A, Bernardini S, Massoud R, Pastore A, et al. (1998) Common mutation in methylenetetrahydrofolate reductase. Correlation with homocysteine and other risk factors for vascular disease. Atherosclerosis 139: 377–383.
- 34. Janosikova B, Pavlikova M, Kocmanova D, Vitova A, Vesela K, et al. (2003) Genetic variants of homocysteine metabolizing enzymes and the risk of coronary artery disease. Mol Genet Metab 79: 167–175.
- 35. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, et al. (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120: 1640–1645.
- 36. Alberti KG, Zimmet P, Shaw J (2006) Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 23: 469–480.
- 37. Choi HK, Curhan G (2004) Beer, liquor, and wine consumption and serum uric acid level: the Third National Health and Nutrition Examination Survey. Arthritis Rheum 51: 1023–1029.
- 38. Kawamoto R, Kohara K, Tabara Y, Miki T, Doi T, et al. (2001) An association of 5,10-methylenetetrahydrofolate reductase (MTHFR) gene polymorphism and common carotid atherosclerosis. J Hum Genet 46: 506–510.
- 39. Lwin H, Yokoyama T, Yoshiike N, Saito K, Yamamoto A, et al. (2006) Polymorphism of methylenetetrahydrofolate reductase gene (C677T MTHFR) is not a confounding factor of the relationship between serum uric acid level and the prevalence of hypertension in Japanese men. Circ J 70: 83–87.
- 40. Dehghan A, Kottgen A, Yang Q, Hwang SJ, Kao WL, et al. (2008) Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study. Lancet 372: 1953–1961.
- 41. Hollis-Moffatt JE, Gow PJ, Harrison AA, Highton J, Jones PB, et al. (2011) The SLC2A9 nonsynonymous Arg265His variant and gout: evidence for a population-specific effect on severity. Arthritis Res Ther 13: R85.
- 42. Tu HP, Chen CJ, Tovosia S, Ko AM, Lee CH, et al. (2010) Associations of a non-synonymous variant in SLC2A9 with gouty arthritis and uric acid levels in Han Chinese subjects and Solomon Islanders. Ann Rheum Dis 69: 887–890.
- 43. Matsuo H, Takada T, Ichida K, Nakamura T, Nakayama A, et al. (2009) Common defects of ABCG2, a high-capacity urate exporter, cause gout: a function-based genetic analysis in a Japanese population. Sci Transl Med 1: 5ra11.
- 44. Yamagishi K, Tanigawa T, Kitamura A, Kottgen A, Folsom AR, et al. (2010) The rs2231142 variant of the ABCG2 gene is associated with uric acid levels and gout among Japanese people. Rheumatology (Oxford) 49: 1461–1465.
- 45. Phipps-Green AJ, Hollis-Moffatt JE, Dalbeth N, Merriman ME, Topless R, et al. (2010) A strong role for the ABCG2 gene in susceptibility to gout in New Zealand Pacific Island and Caucasian, but not Maori, case and control sample sets. Hum Mol Genet 19: 4813–4819.
- 46. Karns R, Zhang G, Sun G, Rao Indugula S, Cheng H, et al. (2012) Genome-wide association of serum uric acid concentration: replication of sequence variants in an island population of the Adriatic coast of Croatia. Ann Hum Genet 76: 121–127.
- 47. Urano W, Taniguchi A, Anzai N, Inoue E, Sekita C, et al. (2010) Association between GLUT9 and gout in Japanese men. Ann Rheum Dis 69: 932–933.
- 48. Onat A, Can G, Ornek E, Altay S, Yuksel M, et al. (2013) Elevated serum uric acid in nondiabetic people mark pro-inflammatory state and HDL dysfunction and independently predicts coronary disease. Clin Rheumatol 32: 1767–1775.
- 49. Alderman M (1999) Uric acid in hypertension and cardiovascular disease. Can J Cardiol 15 Suppl F: 20F–22F.
- 50. Nakashima M, Uematsu T, Kosuge K, Kanamaru M (1992) Pilot study of the uricosuric effect of DuP-753, a new angiotensin II receptor antagonist, in healthy subjects. Eur J Clin Pharmacol 42: 333–335.
- 51. Stiburkova B, Bleyer AJ (2012) Changes in serum urate and urate excretion with age. Adv Chronic Kidney Dis 19: 372–376.