Dietary, anthropometric, and biochemical determinants of uric acid in free-living adults

1475-2891-12-11 1475-2891 Research Dietary, anthropometric, and biochemical determinants of uric acid in free-living adults de OliveiraErick Pradoerick_po@yahoo.com.br MoretoFernandofer_moreto@yahoo.com.br SilveiraLiciana Vaz de Arrudaliciana@ibb.unesp.br BuriniRoberto Carlosburini@fmb.unesp.br

Center for exercise metabolism and nutrition (CeMENutri) Department of Public Health Botucatu School of Medicine (UNESP), Botucatu, Brazil

Department of Pathology Botucatu School of Medicine (UNESP), Botucatu, Brazil

Department of Bioestatistic Bioscience Institute (UNESP), Botucatu, Brazil

CeMENutri Departamento de Saúde Pública Faculdade de Medicina UNESP, Distrito de Rubião Jr. s/n°, Botucatu, SP, 18.618-970, Brazil

Nutrition Journal 1475-2891 2013 12 1 11 http://www.nutritionj.com/content/12/1/11 10.1186/1475-2891-12-1123311699 182201211120131212013 2013de Oliveira et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Uric acid Diet Body composition Inflammation Metabolic syndrome components

Abstract

Background

High plasma uric acid (UA) is a prerequisite for gout and is also associated with the metabolic syndrome and its components and consequently risk factors for cardiovascular diseases. Hence, the management of UA serum concentrations would be essential for the treatment and/or prevention of human diseases and, to that end, it is necessary to know what the main factors that control the uricemia increase. The aim of this study was to evaluate the main factors associated with higher uricemia values analyzing diet, body composition and biochemical markers.

Methods

415 both gender individuals aged 21 to 82 years who participated in a lifestyle modification project were studied. Anthropometric evaluation consisted of weight and height measurements with later BMI estimation. Waist circumference was also measured. The muscle mass (Muscle Mass Index – MMI) and fat percentage were measured by bioimpedance. Dietary intake was estimated by 24-hour recalls with later quantification of the servings on the Brazilian food pyramid and the Healthy Eating Index. Uric acid, glucose, triglycerides (TG), total cholesterol, urea, creatinine, gamma-GT, albumin and calcium and HDL-c were quantified in serum by the dry-chemistry method. LDL-c was estimated by the Friedewald equation and ultrasensitive C-reactive protein (CRP) by the immunochemiluminiscence method. Statistical analysis was performed by the SAS software package, version 9.1. Linear regression (odds ratio) was performed with a 95% confidence interval (CI) in order to observe the odds ratio for presenting UA above the last quartile (♂UA > 6.5 mg/dL and ♀ UA > 5 mg/dL). The level of significance adopted was lower than 5%.

Results

Individuals with BMI ≥ 25 kg/m² OR = 2.28(1.13-4.6) and lower MMI OR = 13.4 (5.21-34.56) showed greater chances of high UA levels even after all adjustments (gender, age, CRP, gamma-gt, LDL, creatinine, urea, albumin, HDL-c, TG, arterial hypertension and glucose). As regards biochemical markers, higher triglycerides OR = 2.76 (1.55-4.90), US-CRP OR = 2.77 (1.07-7.21) and urea OR = 2.53 (1.19-5.41) were associated with greater chances of high UA (adjusted for gender, age, BMI, waist circumference, MMI, glomerular filtration rate, and MS). No association was found between diet and UA.

Conclusions

The main factors associated with UA increase were altered BMI (overweight and obesity), muscle hypotrophy (MMI), higher levels of urea, triglycerides, and CRP. No dietary components were found among uricemia predictors.

Introduction

Uric acid (UA) is a waste product of the human purine balance. It is formed by adenosine, inosine, hypoxanthine, adenine and guanine 1 . Additionally, UA is the main hydrophilic antioxidant in our organism 2 as it is responsible for up to 2/3 of the total antioxidant capacity in human blood 3 . UA exerts a protective action on vitamins C and E 4 and inhibits free radicals, such as radical peroxyl and peroxynitrite, thus protecting cell membrane and DNA 5 6 . However, while acute increases seem to provide antioxidant protection, chronic UA increases are associated with higher risk for coronary artery disease (infarction) 7 8 9 .

High urate concentrations are associated with articular deposits and the onset of (gout) arthritis 10 , metabolic syndrome (MS) 11 , and with cardiovascular diseases 12 . It is known that anthropometric parameters, dyslipidemia, hypertension, inflammation, and insulin resistance can increase the UA concentration 12 .

Other confounding factor is the diet and the relationship between diet and UA has not been fully elucidated, since most studies do not measure urate basal concentrations, do not exclude confounding factors and do not correctly evaluate ingested nutrients 13 . However, some studies shows that the diet can influence the high UA concentration, whereas a positive association is observed among high intake of meat and seafood and a negative association with the dairy product intake is observed 13 14 15 .

Some confounding factors can influence the hyperuricemia because several variables are associated with each other 16 17 18 19 , what makes it difficult to know which component can influence isolated the UA concentration, for example, adiposity markers are associated with higher insulin resistance and leptin production, and both reduce renal uric acid excretion, thus increasing its concentration. HDL-c concentration is negatively associated to insulin resistance, and is found an indirect association with uric acid concentration. Furthermore, obese individuals usually presents metabolic syndrome diagnostic, which can also increase uric acid serum concentrations due to synthesis increase (triglycerides concentration) and lower excretion (hypertension). Many dietary factors are associated with metabolic syndrome components and also can influence indirectly the urate. Additionally, obesity and muscle mass (MM) reduction are associated with low-intensity chronic inflammation, and uric acid levels can increase in order to protect the organism against the moderate oxidative stress resulting from this situation 20 .

Uric acid may be beneficial (antioxidant) or deleterious in high concentrations. Hence, the manegement of UA serum concentrations is promising in the treatment of certain diseases 21 . Therefore, diet, body composition and factors related to the metabolic syndrome are determinant in UA increase, but no studies have gathered all these factors or conjointly evaluated these components in order to learn which of them are actually mainly responsible for UA increase.

Hence, the present study aimed to evaluate what are the main factors associated with higher uricemia values, analyzing diet, body composition and biochemical markers.

Individuals

A descriptive cross-sectional study was conducted in a subgroup of participants clinically screened for the lifestyle modification program “Mexa-se Pró-Saúde [Move for Health]” from 2002 to 2006. This program is offered to patients with non-communicable chronic diseases and consists of regular physical exercise and nutritional counseling. The Center for Physical and Nutritional Metabolism (CeMENutri) has conducted this program in Botucatu since 1992. Botucatu is a city located in mid-São Paulo State, approximately 230 km west of the capital city. It has a population of 121,274 habitants 22 .

The inclusion criteria for participants were individuals of both genders with at without metabolic or motor disabilities that would limit physical exercise.

A convenience sample was consisted of 1,075 individuals who were 51.6 ± 10.2 years old, and 59% of them were females. All the subjects signed a free-consent form, and the research project was approved by the Research Ethics Committee (document no. CEP 3272–2009) of the Botucatu School of Medicine (FMB), Univ Estadual Paulista - UNESP, Brazil. Of the 1,075 subjects, 415 had biochemical, anthropometric and dietetic data.

Dietary intake

Dietary intake data was determined by using 24-hour recalls. The diet was documented by trained professionals, and in order to obtain accurate information, the subjects were asked how often they usually ate during the day, what food varieties were consumed, how food was prepared, what the serving size was, and what food/meal brands were consumed. The diets were analyzed by NutWin® software (2002), version 1.5 23 , and the main nutrients of interest were energy, protein, fat (saturated, mono and polyunsaturated), cholesterol, carbohydrates, and dietary fiber. Mean individual nutrient intakes per day were computed using the NutWin database and Brazilian food tables 24 25 26 . The Healthy Eating Index (HEI) modified for the Brazilian population was used to assess the quality of the participant’s diet 27 . The original HEI was developed based on a 10-component system of five food groups with a total possible index score of 100. This method was adapted for the Brazilian population based on the Brazilian food guide, which has eight food groups and 12 components to measure food intake variety. Each of the 12 components has a score ranging from 0 to 10; therefore the total possible index score is 120.

Anthropometry

Body weight was measured by a platform-type anthropometric scale (Filizola®) with a maximum capacity of 150 kg and an accuracy of 0.1 kg. Height was determined by a portable Seca® stadiometer with accuracy of 0.1 cm 28 . By using body-weight and height measurements, the Body Mass Index (BMI) was calculated.

Waist circumference (WC) was measured at the point midway between the last rib and the iliac crest. A steel Sanny® anthropometric tape measure (without a lock) was used for all measurements.

A bioelectrical impedance device (Biodynamics®, model 450, USA) was used to determine body fat percentage (% body fat) 29 and body-muscle mass, whose data were used for calculating the muscle-mass index (MMI) 30 .

Clinical evaluation of arterial blood pressure

Systolic and diastolic arterial blood pressure was evaluated with the individual in the seated position according to the procedures described by the VI Brazilian Guidelines on Arterial Hypertension 31 . Values of systolic blood pressure ≥ 130 mm Hg and/or diastolic blood pressure ≥85 mm Hg were considered to be abnormal 32 33 .

Biochemical analyses

Blood samples were collected after overnight fasting for 10 to 12 hours using a vacuum venous puncture. The individuals were previously instructed to not perform vigorous physical exercise 24 hours and/or not drink alcohol 72 hours before collection. Laboratory analyses of lipid parameters (total cholesterol, fractions and triglycerides), glucose, uric acid, urea, gamma-glutamyl transferase (γ-GT), albumin and total proteins were performed within 4 hours after blood collection using the Dry- Chemistry method (Vitros® system, Johnson & Johnson) while C-reactive protein (CRP) was measured by a high-sensitive Chemiluminescent method (Siemens Diagnostics), at the Clinical Analyses Laboratory of the Botucatu School of Medicine University Hospital - UNESP in Botucatu/SP. Low density lipoprotein (LDL-c) was calculated by Friedewald equation (LDL-c = TC- (HDL-c + TG/5) 34 . Glomerular filtration rate was calculated 35 and added in adjustments.

Metabolic syndrome

Diagnosis of the metabolic syndrome was performed according to the NCEP-ATP III criteria 32 33 with adaptation for glucose values 36 . The 5 components used were high systolic and diastolic blood pressure and WC measurements, and plasma levels of triglycerides, HDL-c and, glucose. The metabolic syndrome was diagnosed when 3 or more of these components were abnormal.

Operational definition of variables

Higher uric acid was considered when was higher than the 4^th quartile (♂ > 6,5 mg/dL and ♀ > 5 mg/dL). Overweight was classified as BMI ≥ 25 kg/m² 37 , altered WC was considered when above 102 cm (40.16 inches) for men and above 88 cm (34.65 inches) for women 32 33 . Low muscle mass was defined by the muscle mass index (MMI) (lower quartile vs. higher quartile). Higher body fat was defined by body fatness higher than 25% for men and higher than 35% for women 38 . Hypertriglyceridemia was defined by plasma concentrations ≥ 150 mg/dL 32 33 , lower HDL-c as < 40 mg/dL for men and < 50 mg/dL for women 32 33 and hypercholesterolemia was > 200 mg/dL 39 . Higher plasma glucose was defined by ≥ 100 mg/dL 36 , higher CRP and gamma-gt (4^th quartile).

Statistical analysis

Statistical analyses were conducted by using the SAS software for windows (SAS version 9.1.3., SAS Institute, Inc., Cary, NC) and STATISTICA 6.0. Sample normality was tested by the Shapiro-Wilk test. Descriptive statistics were performed for the study, and continuous variables are presented as means ± standard deviation (SD) or median and interquartile intervals. UA values were divided into quartiles for both genders, and one-way ANOVA and generalized linear model considering gamma distribution and link function log were performed in order to observe the difference between variables according to the increase in the UA quartile. Logistic regression (Odds ratio) was made in order to determine the probability of UA increase (♂UA > 6.5 mg/dL and ♀ UA > 5 mg/dL) by food intake, anthropometry and biochemical analysis. Backward stepwise multiple regression analysis was used to determine the main components responsible for UA increase according to gender. A p value < 0.05 was adopted as significant.

Results

Higher UA concentrations (last quartile) were associated with higher values for body adiposity markers (weight, BMI, WC and % body fat) whilst lower UA concentrations (Q1) were associated with higher MMI values when compared to the other quartiles. There was no difference in relation to the individuals’ age or height (Table 1).

Table 1

Quartile 1

Quartile 2

Quartile 3

Quartile 4

Different letters = P < 0.05; a,b,c= Anova one-way.

Age (years)

53,4 ± 9.7^a

52.7 ± 9.8^a

51.7 ± 9.5^a

54.1 ± 9.4^a

Weight (kg)

69.3 ± 13.3^a

71.5 ± 12.7^a

76.8 ± 16^b

81.6 ± 17^c

Height (m)

1.62 ± 0.08^a

1.61 ± 0.08^a

1.62 ± 0.1^a

1.63 ± 0.08^a

Body Mass Índex (kg/m ² )

26.17 ± 3.7^a

27.2 ± 3.9^a

29.0 ± 5.2^b

30.5 ± 5.2^c

Waist Circumference (cm)

87.64 ± 11.9^a

90.9 ± 11.1^b

94.2 ± 13^c

99.4 ± 13.7^d

% of body fat

27.8 ± 5.6^a

29.3 ± 7.8^ab

31 ± 8.4^b

33.6 ± 8.5^c

Muscle Mass Índex (kg/m ² )

10.8 ± 4.1^a

8.8 ± 4^b

8.7 ± 3.9^b

8.8 ± 4^b

Total Energy Intake (kcal)

1612 ± 608^a

1584 ± 603^a

1651 ± 700^a

1581 ± 613^a

Carbohydrate (%)

51.05 ± 9.7^a

51.7 ± 10.8^a

50.8 ± 9.1^a

51 ± 10.7^a

Protein (%)

18.8 ± 6.11^a

19.1 ± 6.2^a

19.3 ± 6.2^a

18.4 ± 5.9^a

Total Lipid (%)

30.17 ± 8.9^a

29.2 ± 9.2^a

30.0 ± 8.1^a

30.4 ± 8.5^a

Saturated Lipid (%)

8.12 ± 4.1^a

7.7 ± 3.6^a

8.4 ± 4.1^a

7.9 ± 3.6^a

Monounsaturated Lipid (%)

8.32 ± 2.95^a

9.0 ± 4.3^a

8.9 ± 3.5^a

9.1 ± 3.5^a

Polyunsaturated Lipid (%)

5.65 ± 2.8^a

6.2 ± 2.8^a

6.1 ± 3.3^a

7.4 ± 3.6^b

Fiber (g)

14.1 ± 7.1^a

13.8 ± 8.6^a

13.3 ± 7.6^a

13.1 ± 8.1^a

Cholesterol (mg)

183 ± 120.8^a

188 ± 126^a

198 ± 124^a

185 ± 135^a

Cereal (servings)

3.35 ± 1.8^a

3.5 ± 1.6^a

3.4 ± 1.8^a

3.4 ± 1.5^a

Fruits (servings)

3 ± 2.73^a

2.8 ± 3.1^a

2.7 ± 3.2^a

2.8 ± 3.0^a

Vegetables (servings)

3.6 ± 2.8^a

2.9 ± 2.5^ab

2.2 ± 2^b

3.1 ± 3.1^a

Legumes (servings)

1.2 ± 1.7^a

1.1 ± 1.1^a

1.1 ± 1.5^a

1.3 ± 1.8^a

Dairy (servings)

1.7 ± 1.7^a

1.4 ± 1.2^a

1.5 ± 1.2^a

1.5 ± 1.3^a

Meat (servings)

2.13 ± 1.6^a

2.3 ± 1.6^a

2.1 ± 1.5^a

1.9 ± 1.5^a

Sugar (servings)

1.67 ± 1.6^a

1.7 ± 1.7^a

2.0 ± 1.9^a

1.5 ± 2.2^a

Oil (servings)

2.4 ± 2.4^a

2.4 ± 2.5^a

2.4 ± 1.9^a

2.7 ± 2.2^a

Variety (number)

14.27 ± 4.2^a

13.3 ± 3.5^a

13.1 ± 3.6^a

13.5 ± 4^a

Health Eat Index (points)

86.4 ± 13^a

83.2 ± 12.5^a

82.3 ± 14.3^a

81.8 ± 15^a

Plasma urea (mg/dL)

30.5 ± 8.5^a

32 ± 10.6^ab

32.2 ± 10.2^ab

34.8 ± 14.4^b

Plasma creatinine (mg/dL)

0.89 ± 0.2^a

0.93 ± 0.2^ab

0.96 ± 0.19^b

1.04 ± 0.3^c

Plasma HDL-c (mg/dL)

60.2 ± 18^a

52.3 ± 15.5^b

49.3 ± 14.3^b

49.4 ± 13.9^b

Plasma LDL-c (mg/dL)

123 ± 35.2^a

132 ± 36.1^ab

134 ± 37.4^b

134 ± 38^b

Plasma glucose (mg/dL)

101 ± 28^a

102 ± 37^a

100 ± 32^a

104 ± 30^a

Plasma total Cholesterol (mg/dL)

208 ± 41^a

213 ± 44^ab

215 ± 40^ab

220 ± 41^b

Plasma triglycerides (mg/dL)

114 (81–171)^a

117 (88–174)^ab

138 (93–183)^bc

177 (124–232)^c

Plasma total Protein (g/dL)

7.08 ± 0.5^a

7.36 ± 0.6^b

7.5 ± 0.6^b

7.52 ± 0.6^b

Plasma albumin (g/dL)

4.51 ± 0.48^a

4.4 ± 0.5^a

4.4 + 0.5^a

Plasma gamma-gt (l/U)

25 (18–32.5)^a

27 (19–57)^ab

29 (21–44)^ab

34 (25.5-51.5)^b

Plasma calcium (mg/dL)

8.8 ± 0.4^a

8.9 ± 0.9^a

9.1 ± 0.4^a

Plasma c-Reactive Protein (mg/dL)

0.14 (0–0.3)^a

0.2 (0.1-0.75)^a

0.26 (0.1-0.5)^a

0.3 (0.18-0.6)^a

Systolic blood pressure (mmHg)

126 ± 17^a

126 ± 18^ab

132 ± 17^b

130 ± 17^ab

Diastolic blood pressure (mmHg)

80 ± 11^a

79 ± 11^a

82 ± 10^a

83 ± 11^a

As regards dietary intake, greater polyunsaturated fat intake was observed in individuals with higher UA quartiles. Smaller intake of vegetables was also observed in the second and third quartiles. The intake of other macronutrients and groups of the pyramid servings did not show difference according to the increase in the UA quartile (Table 1).

Urea, creatinine, LDL-c, TC, TG, total proteins and gamma-GT showed lower values according to the increase of the UA quartiles. The opposite was observed for HDL-c, with individuals in the first quartile showing higher concentrations. No significant difference was observed for the other biochemical parameters. Sistolyc blood pressure showed a higher value in the third quartile while there was no difference for diastolic blood pressure (Table 1).

The odds ratio for the UA at the last quartile (♂ UA > 6.5 mg/dL and ♀ UA > 5 mg/dL) was performed, according to alterations in body composition. In model 1, additionally to the adjustment for gender and age, all body composition components were included in the same model, because it was possible to observe the influence of each compartment alone. Individuals with BMI, WC and % body fat above normality as well as with little muscle mass (MMI < P25) showed higher chances of high uric acid. When MS was adjusted for BMI, % body fat and MMI (Model 2) remained significant, and WC analysis was not performed, since it is part of the MS criteria. The four body-composition components evaluated remained significant when MS was removed from the adjustment and CRP (inflammation) gamma-gt LDL-c, glomerular filtration (creatinine and urea) were added. When the MS components (HDL-c, TG, hypertension and glucose) were added to the adjustment, only high BMI and low muscle mass remained significant (Table 2).

Table 2

Model 1

Model 2

Model 3

Model 4

Model 1 – Adjusted for gender, age and other components of body composition analyzed.

Model 2 – Model 1 + Metabolic Syndrome.

Model 3 – Model 1 + c-Reactive Protein, gamma-gt, LDL-c, creatinine, urea and albumin.

Model 4 – Model 3 + HDL-c, triglycerides, hypertension and glucose.

*p < 0.05.

Body Mass Index (abnormal vs normal)

2.23 (1.32-3.75)*

2.81 (1.5-5.28)*

1.97 (1.41-3.41)*

2.28(1.13-4.6)*

Muscle Mass Index (P25 vs P75)

8.11 (4.52-14.58)*

17.51 (7.98-38.48)*

7.16 (3.6-14.29)*

13.4 (5.21-34.56)*

Waist Circumference (abnormal vs normal)

2.21 (1.39-3.52)*

1.91 (1.15-3.16)*

1.14 (0.6-2.17)

% body fat (abnormal vs normal)

1.96 (1.22-3.16)*

1.8 (1.03-3.16)*

1.76 (1.04-2.98)*

1.54 (0.81-2.9)

As regards dietary intake, intake >60% of carbohydrate of total caloric intake when compared to <50% of CHO, represented higher chances of UA increase (Model 1). It is known that high CHO intake can increase glycemia and/or TG, and these may be related to UA increase. Hence, both were adjusted (Models 2 and 3). When high CHO intake was adjusted for glycemia or TG, the effect of CHO on UA was lost. Thus CHO intake could directly influence UA serum concentrations, firstly increasing glycemia or TG (Table 3). The intake of food pyramid servings and HEI did not show influence on UA (data not shown).

Table 3

Model 1

Model 2

Model 3

Model 1 – adjusted for gender, age, Body Mass Index, Total Energy Intake and other components of diet (macronutrient) analyzed.

Model 2 – Model 1 + glucose.

Model 3 – Model 1 + triglycerides.

*p < 0,05.

Carbohydrate ( >60% vs < 50%)

3.22 (1.01-10.27)*

1.38 (0.22-8.73)

1.43 (0.23-9.0)

Protein (>15% vs <15%)

0.99 (0.45-2.18)

1.05 (0.36-3.05)

0.83 (0.28-2.45)

Total Lipid (>35% vs 25-35%)

1.57 (0.62-4.02)

1.02 (0.27-3.86)

0.88 (0.23-3.36)

Saturated lipid (>10% vs <10%)

0.77 (0.35-1.70)

0.32 (0.10-1.0)

0.34 (0.11-1.06)

Monounsaturated Lipid (>10% vs <10%)

1.38 (0.64-2.98)

2.51 (0.71-8.89)

2.02 (0.56-7.34)

Polyunsaturated Lipid (>10% vs <10%)

1.87 (0.82-4.29)

2.05 (0.62-6.82)

2.51 (0.75-8.39)

Cholesterol (>300 vs <300 mg)

1.51 (0.61-3.76)

1.99 (0.33-11.95)

1.25 (0.20-7.83)

Fiber (Q4 vs Q1)

1.06 (0.44-2.55)

1.99 (0.45-8.77)

1.51 (0.33-6.85)

The biochemical data were gathered in the same model, and adjusted for gender and age (Model 1) was added. The altered values for creatinine, HDL-c, LDL-c and TG, conjointly with values in the last quartile for gamma-gt and CRP were significant in increasing the chances of individuals’ showing high UA. When body composition was added to the adjustment (Model 2), the creatinine and HDL-c values lost their effect; however, gamma-gt, LDL-c and TG remained significant. MS and glomerular filtration rate were also added to the adjustment (Model 3); TG remained significant, and the last CRP quartile and altered urea values became significant, thus increasing the chances of UA increase. Hypertension and glomerular filtration rate were added to the model, and MS was removed (Model 4) because, in this way, all MS components would be gathered in the same adjustment model, and no modifications were observed in relation to the latter model, that is, altered urea and TG values; and values above the CRP last quartile were associated with UA increase (Table 4).

Table 4

Model 1

Model 2

Model 3

Model 4

Model 1 – adjusted for gender, age and other biochemical components analyzed.

Model 2 – Model 1 + Body Mass Index, Muscle Mass Index, waist circumference and% body fat.

Model 3 – Model 2 + Metabolic Syndrome and glomerular filtration rate.

Model 4 – Model 2 + Hypertension and glomerular filtration rate.

*p < 0,05.

Creatinine (abnormal vs normal )

3.25 (1.09-9.69)*

1.99 (0.47-8.37)

0.83 (0.12-5.69)

0.83 (0.12-5.67)

Urea (abnormal vs normal)

1.25 (0.84-1.86)

1.35 (0.75-2.41)

2.72 (1.27-5.83)*

2.53 (1.19-5.41)*

Albumin (Q4 vs Q1)

1.4 (0.9-2.21)

0.74 (0.38-1.42)

1.05 (0.41-2.72)

1.11 (0.43-2.87)

HDL-c (abnormal vs normal)

1.57 (1.12-2.17)*

1.21 (0.77-1.90)

0.98 (0.53-1.81)

1.17 (0.66-2.08)

Gamma-gt (Q4 vs Q1)

4.58 (2.22-9.44)*

3.41 (1.08-10.7)*

3.53 (0.91-13.61)

3.33 (0.87-12.73)

Glucose (abnormal vs normal)

1.05 (0.76-1.44)

1.01 (0.64-1.57)

1.03 (0.56-1.87)

1.27 (0.74-2.18)

LDL-c (abnormal vs normal)

1.4 (1.04-1.88)*

1.52 (1.02-2.28)*

1.16 (0.71-1.90)

1.15 (0.70-1.89)

C-Reactive Protein (Q4 vs Q1)

2.37 (1.43-3.91)*

1.81 (0.91-3.58)

2.87 (1.10-7.45)*

2.77 (1.07-7.21)*

Triglycerides (abnormal vs normal)

1.83 (1.34-2.50)*

2.44 (1.59-3.83)*

2.29 (1.21-4.30)*

2.76 (1.55-4.90)*

The main factors of each compartment were gathered in order to observe which factor showed greater influence on UA serum concentrations according to gender. It was observed that WC and creatinine increase, together with HDL-c and muscle mass reduction, is 36% responsible for UA increase in females. In males, however, BMI increase and muscle mass decrease showed 28% responsibility (Table 5).

Table 5

Female

Male

Betaβ

intercept

−0.638

0.713

−0.894

0.372

intercept

1.709

0.783

2.183

0.031

Waist Circumference (cm)

0.475

0.057

0.051

0.006

8.303

0.000

Body Mass Index (kg/m ² )

0.526

0.081

0.189

0.029

6.538

0.000

Muscle Mass (kg)

−0.393

0.057

−0.040

0.006

−6.925

0.000

Muscle Mass (kg)

−0.415

0.081

−0.041

0.008

−5.152

0.000

Creatinine (mg/dL)

0.242

0.047

2.244

0.440

5.100

0.000

HDL-c (mg/dL)

−0.197

0.049

−0.016

0.004

−4.046

0.000

R ²

0.36

0.00

R ²

0.28

0.00

Discussion

The main finding of the study was that higher UA levels were associated positively with BMI, triglycerides, urea, and CRP and inversely with MMI. According to the gender, the main predictors for UA increase were BMI and muscle mass for men. Waist circumference, creatinine, and muscle mass (positively); and HDL-c (negatively) were associated for women.

UA serum concentrations are maintained through the synthesis and excretion of urate 40 ; hence, creatinine and serum urea, which are considered to be glomerular function markers, are directly related to UA concentrations, particularly due to the urate excretion relationship. Choe et al., 2008 41 reported that creatinine has strong influence on urate concentrations. Rathmann et al. (2007) investigated UA concentrations in a population consisting of black and white adults for ten years and identified an independent relationship of creatinine and urate 42 .

Creatinine serum concentration was one of the main UA-related factors for females. In the odds-ratio model (Table 5), altered creatinine increased the chances of hyperuricemia, but this fact occurred only in the first adjustment of the model. When the body-composition components were added, the effect was lost. On the other hand individuals with altered urea showed approximately 2.5 more chances of increasing UA, regardless of the influence from other factors, including glomerular filtration rate, which means that mechanisms independently of excretion can influence this relation.

The increase in uricemia distribution quartiles did not follow age increase. It is usually reported that, as age advances, a gradual reduction in glomerular filtration occurs, which results in plasma retention of solutes that are normally excreted by the kidneys 43 . In the present study, age ranged from 21 to 82 years, but most individuals were 40 to 60 years old and probably showed little variability in glomerular filtration. Additionally, the adjustment for glomerular filtration rate was made.

The increase in uricemia distribution quartiles was accompanied by increase in body adiposity markers (weight, BMI, WC and % body fat). Altered WC was positively associated with urate concentrations; however, when fitted for other MS components, the effect was lost, thus showing that WC is indirectly associated with UA, since individuals with abdominal adiposity could present MS and/or alteration in its components, and these could be responsible for affecting UA. It is believed that TG is the main MS component influencing UA, as it was the only factor that remained significant after all adjustments.

Other studies showed the relationship between WC or abdominal adiposity and UA increase 44 45 , reporting that visceral fat is more related to UA increase than subcutaneous fat 46 47 . Adipose tissue produces various cytokines, including leptin, and the probable explanation for the association between WC and hyperuricemia would be the association found by Bedir et al., (2003) 48 and Fruehwald-Schultes et al., (1999) 49 , where UA serum concentrations are independently related to leptin. Two mechanisms could explain such relationship. The first would be the influence of leptin on the renal function, which decreases renal UA excretion. In the second mechanism, UA could modulate leptin concentrations, thus increasing its gene expression or decreasing its excretion 49 .

BMI above 25 kg/m² was one of the main components associated with UA increase both in the fitted models and in the multiple regression analysis for males, and it also showed significant correlation with uricemia. BMI showed a positive relation with leptin concentrations 50 , which is a factor leading to UA increase.

Additionally, individuals with high BMI may show insulin resistance, TG alteration and high blood pressure, and all these factors are related to UA increase 12 . In our study, insulin sensitivity was not measured; however, the analyses were fitted for SAH and TG, and the values remained significant. This showed that independent mechanisms from these two factors could explain such relation, probably to insulin resistance.

UA increase is observed in individuals with insulin resistance, probably because hyperinsulinemia would cause lower renal UA excretion 51 . Besides, insulin could also indirectly affect UA, since there is an association between hyperinsulinemia and hypertriglyceridemia.

In the present study, individuals with altered TG showed approximately 2.5 more chances of UA increase, regardless of other variables (body composition. gender, inflammation, dyslipidemia, MS, and SAH). Some studies show that high plasma triglyceride concentrations are related to hyperuricemia 52 53 54 55 . One of the explanations for such relation would be that, during the TG synthesis, there would be a greater need for NADPH for the de novo synthesis of fatty acids 53 . Matsuura et al. (1998) report that the synthesis of fatty acids in the liver is related to the de novo synthesis of purine, thus accelerating UA production 45 .

In the present study, UA concentrations were negatively determined by MM (kg) in males and females. The hypothesis would be the negative correlation existing between MM and inflammation 56 , since an association between UA increase and inflammatory markers has been reported 57 . However, in our study, muscle mass was fitted for inflammation (CRP), and the influence of muscle mass on UA remained. Possibly, other mechanisms would influence the association between muscle reduction and UA increase, such as oxidative stress.

A recent study observed an inverse relation between MMI and UA in healthy individuals older than 40 years 58 . The authors believe that increased urate serum concentrations would be a causal factor for sarcopenia, especially through increased inflammation and oxidative stress 58 . During the sarcopenic process, reactive oxygen species (ROS) and oxidative stress increase, and one of the mechanisms for ROS increase would be the activation of the xanthine oxidase metabolic pathway, which increases UA production and the superoxide radical 59 . In the present study, oxidative stress was not evaluated, and it may be a causal factor for such relation between UA and MMI; however, further studies analyzing the cause and effect between these two factors and their main mechanisms are necessary.

A positive association was found between UA and CRP (inflammation), regardless of body composition (hyperadiposity and/or sarcopenia), gender, age and the presence of MS and its components, which means that there are a direct relation between these two factors. Another study showed a positive association between inflammation and UA, but no adjustment was performed to observe whether the effect would remain the same 60 . In-vitro studies showed that UA exerts a pro-inflammatory effect, thus stimulating the production of interleukin-1, interleukin-6 the tumor necrosis factor 61 .

Individuals with higher CRP concentrations showed increased UA; however, UA is positively associated with total plasma antioxidant capacity, thus showing beneficial effects. On the other hand, deleterious relations of urate, such as the inverse association with adiponectin and the positive relation with E-selectin, were previously observed 62 . Such associations can show that UA may increase in order to enhance total plasma antioxidant capacity against moderate oxidative and inflammatory stress, thus being a protective feature against factors related to cardiovascular diseases. It is also noteworthy that UA may be deleterious in high concentrations 21 , which shows the importance of maintaining urate values within normality.

Reduced HDL-c was one of the main factors responsible for UA increase (negative association) in females. This fact was observed by the backward stepwise multiple regression analysis. The negative correlation between HDL-c and AU was previously described 60 , and it has been recently shown that the higher the urate concentration, the smaller the size of HDL-c and LDL-c particles, which provides a greater atherogenic profile 60 . However, our group has shown that, when the adjustment for body composition and SM components is performed, the association between UA and HDL-c is lost 16 , which was also observed in the present study after adjustment. The probable mechanisms for the inverse association between these two factors would be the relation existing between HDL-c reduction and insulin resistance 63 .

It is expected that individuals with high UA will have more chances of presenting MS 52 . MS is associated with increased oxidative stress 64 , and it known that UA is a potent antioxidant 3 . Thus, it can be supposed that urate increase could result from the defense mechanism from such oxidative stress 65 . These factors further enhance our results, since the factors associated with UA (BMI, MMI, urea, TG and CRP) remained significant even after adjustment for MS.

In the present study, diet did not show direct influence on UA, but inadequate diet, conjointly with lack of physical activity, could alter body composition (higher adiposity), and such alteration would change UA. Additionally, an indirect relation of high carbohydrate intake was observed through the possible alterations in TG and/or glycemia.

The intake of protein, meat and legumes, which could be related to increased purine intake, was not related to UA concentrations, thus agreeing with the literature 13 14 . Some studies showed that high purine intake does not influence UA, since a purine-rich diet would be responsible for increasing only 1 to 2 mg/dL of UA 66 67 . Although the present study evaluated the intake of protein-source foods, an exclusive investigation on purine-source foods was not performed. Hence, further studies are required in order to learn about the actual effects of the intake of purine-rich foods on urate concentrations.

A significant weak and inverse correlation was observed (r = −0.11) between the intake of dairy products and UA, but when fitted for gender, BMI and VCT, the significance was lost. Although the present study did not observe such relation, other investigations reported an inverse association between dairy products and UA 13 14 68 , which is explained by the fact that milk proteins (lactalbumin and casein) showed a uricosuric effect 69 .

Based on our data, lifestyle-modification conducts targeted at lean-mass maintenance and fat-mass reduction and concern about dietary composition are suggested in primary care for uricemia increase in non-uremic individuals.

Study limitations

This was a cross-sectional study, and some cause/effect relationships cannot be possibly confirmed, but only whether or not an association between the studied factors exists. The dietary intake investigation on the individuals was performed only on one day, and a food recall for at least three weekdays would ideal, since there may be dietary variations that were not analyzed. Some dietary factors were not evaluated, such as the intake of alcohol, purine and caffeinated drinks, which are known for their interference with UA values 14 68 70 . Insulin resistance was not measured, and that would be important for this type of analysis, since some UA increase mechanisms are involved with this factor. Furthermore, medication intake and smoking status were not controlled.

Conclusions

Abbreviations

BMI: Body mass index; MMI: Muscle Mass Index; WC: Waist circumference; NCEP-ATPIII: National Cholesterol Education Program-Adult Treatment Painel III; MS: Metabolic Syndrome; γ–GT: γ-glutamyl transferase; CRP: C-reactive protein; TG: Triglycerides.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

EPO wrote the manuscript, FM corrected the manuscript, LVAS made the statistical analysis and corrected the manuscript. RCB read, corrected, and approved the final version of the manuscript. All authors read and approved.

Acknowledgments

CNPq, CAPES and FAPESP for the financial support and GAP (a statistical support of Botucatu School of Medicine) for the statistical analysis.

Purine salvage pathways in myocardiumManfrediJPHolmesEWAnnu Rev Physiol19854769170510.1146/annurev.ph.47.030185.0033552986542Uric acid: the oxidant-antioxidant paradoxSautinYYJohnsonRJNucleosides Nucleotides Nucleic Acids20082760861910.1080/15257770802138558289591518600514Antioxidant status in patients with uncomplicated insulin-dependent and non-insulin-dependent diabetes mellitusMaxwellSRThomasonHSandlerDLeguenCBaxterMAThorpeGHJonesAFBarnettAHEur J Clin Invest19972748449010.1046/j.1365-2362.1997.1390687.x9229228The effects of vitamin C and urate on the oxidation kinetics of human low-density lipoproteinMaYSStoneWLLeClairIOProc Soc Exp Biol Med199420653598183962Uric acid, a natural scavenger of peroxynitrite, in experimental allergic encephalomyelitis and multiple sclerosisHooperDCSpitsinSKeanRBChampionJMDicksonGMChaudhryIKoprowskiHProc Natl Acad Sci U S A19989567568010.1073/pnas.95.2.675184799435251The relative contributions of vitamin E, urate, ascorbate and proteins to the total peroxyl radical-trapping antioxidant activity of human blood plasmaWaynerDDBurtonGWIngoldKUBarclayLRLockeSJBiochim Biophys Acta198792440841910.1016/0304-4165(87)90155-33593759Hyperuricemia and the risk for subclinical coronary atherosclerosis - data from a prospective observational cohort studyKrishnanEPandyaBJChungLDabbousOArthritis Res Ther201113R6610.1186/ar3322313206121501486Hyperuricemia and risk of stroke: a systematic review and meta-analysisKimSYGuevaraJPKimKMChoiHKHeitjanDFAlbertDAArthritis Rheum20096188589210.1002/art.24612271426719565556Metabolic Syndrome and dietary components are associated with coronary artery disease risk score in free-living adults: a cross-sectional studyTakahashiMMde OliveiraEPde CarvalhoALDantasLABuriniFHPortero-McLellanKCBuriniRCDiabetol Metab Syndr20113710.1186/1758-5996-3-7310341721554698Epidemiology of goutRoddyEDohertyMArthritis Res Ther20101222310.1186/ar3199304652921205285Serum uric acid is a determinant of metabolic syndrome in a population-based studyOnatAUyarelHHergencGKarabulutAAlbayrakSSariIYaziciMKelesIAm J Hypertens2006191055106210.1016/j.amjhyper.2006.02.01417027827Effects of xanthine oxidase inhibition with allopurinol on endothelial function and peripheral blood flow in hyperuricemic patients with chronic heart failure: results from 2 placebo-controlled studiesDoehnerWSchoeneNRauchhausMLeyva-LeonFPavittDVReaveleyDASchulerGCoatsAJAnkerSDHambrechtRCirculation20021052619262410.1161/01.CIR.0000017502.58595.ED12045167Dietary factors associated with hyperuricemia in adultsYuKHSeeLCHuangYCYangCHSunJHSemin Arthritis Rheum20083724325010.1016/j.semarthrit.2007.04.00717570471Purine-rich foods, dairy and protein intake, and the risk of gout in menChoiHKAtkinsonKKarlsonEWWillettWCurhanGN Engl J Med20043501093110310.1056/NEJMoa03570015014182Intake of purine-rich foods, protein, and dairy products and relationship to serum levels of uric acid: the Third National Health and Nutrition Examination SurveyChoiHKLiuSCurhanGArthritis Rheum20055228328910.1002/art.2076115641075Dietary, anthropometric, and biochemical determinants of plasma high-density lipoprotein-cholesterol in free-living adultsde OliveiraEPMandaRMTorezanGACorrenteJEBuriniRCCholesterol20112011851750306586821490775Association of dyslipidemia with intakes of fruit and vegetables and the body fat content of adults clinically selected for a lifestyle modification programTakahashiMMde OliveiraEPMoretoFPortero-McLellanKCBuriniRCArch Latinoam Nutr20106014815421425719Muscle mass gain after resistance training is inversely correlated with trunk adiposity gain in postmenopausal womenOrsattiFLNahasEAOrsattiCLde OliveiraEPNahas-NetoJda MotaGRBuriniRCJ Strength Cond Res2012262130213910.1519/JSC.0b013e318239f83721986696Pathological and Behavioral Risk Factors for Higher Serum C-Reactive Protein Concentrations in Free-Living Adults-a Brazilian Community-Based StudyMoretoFde OliveiraEPMandaRMTorezanGATeixeiraOMichelinEBuriniRCInflammation2012in pressHigh plasma uric acid concentration: causes and consequencesde OliveiraEPBuriniRCDiabetol Metab Syndr201241210.1186/1758-5996-4-12335927222475652Altered uric acid levels and disease statesKutzingMKFiresteinBLJ Pharmacol Exp Ther20083241717890445IBGEInstituto Brasileiro de Geografia e Estatística. Estimativas da população residente em municípios brasileiros. Ministério do Planejamento, Orçamento e Estatística, 2006 [acesso em 12 de junho de 2008, disponível em http://www.ibge.gov.br/home/estatistica/populacao/estimativa2006/estimativa.shtm AnçãoMSCuppariLDraibeASSigulemDPrograma de Apoio à Nutrição Nutwin Versão 1,5São Paulo: Departamento de Informática em Saúde (DIS) da Escola Paulista de Medicina da Universidade Federal de São Paulo200217279285NEPA/UNICAMPTabela brasileira de composição de alimentos. Taco. Versão 1. Campinas2004(http://189.28.128.100/nutricao/docs/taco/tab_bras_de_comp_de_alim_doc.pdf)IBGETabela de Composição de AlimentosRio de Janeiro51999(http://biblioteca.ibge.gov.br/visualizacao/monografias/GEBIS%20-%20RJ/endef/1999_Tabela%20de%20composicao%20de%20alimentos.pdf)PhilippiSTTabela de Composição de Alimentos: Suporte para decisão nutricionalSão Paulo: Coronário2Âªth2002Adaptation of the healthy eating index to the food guide of the Brazilian populationMotaJFRinaldiAEMPereiraAFMaestáNScarpinMMBuriniRCRev Nutr20082154555210.1590/S1415-52732008000500007HeywardVHStolarczykLMAvaliação da composição corporal aplicadaBarueri: Manole1200022995101Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation studySegalKRVan LoanMFitzgeraldPIHodgdonJAVan ItallieTBAm J Clin Nutr1988477143337041Estimation of skeletal muscle mass by bioelectrical impedance analysisJanssenIHeymsfieldSBBaumgartnerRNRossRJ Appl Physiol20008946547110926627VI Diretrizes Brasileiras de Hipertensão“Sociedade Brasileira de Cardiologia / Sociedade Brasileira de Hipertensão / Sociedade Brasileira de NefrologiaArq Bras Cardiol2010951 supl.115121340292Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III)Jama200128524862497 http://www.ncbi.nlm.nih.gov/pubmed/11368702 10.1001/jama.285.19.248611368702Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final reportCirculation200210631433421 http://www.ncbi.nlm.nih.gov/pubmed/12485966 12485966Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifugeFriedewaldWTLevyRIFredricksonDSClin Chem1972184995024337382Prediction of creatinine clearance from serum creatinineCockcroftDWGaultMHNephron197616314110.1159/0001805801244564Diagnosis and classification of diabetes mellitusAmerican Diabetes AssociationDiabetes Care2004271510WHOReport of a joint FAO/WHO Consultation. Preparation and use of food-based Dietary GuidelinesGeneva: World Health Organization2002An approach to the classification and evaluation of obesityBrayGObesityNew York: JB Lippincott CompanyBjörntorp P, Brodoff BN1992294308Association of oxidative stress and PON1 with LDL and HDL particle size in middle-aged subjectsVekicJKotur-StevuljevicJJelic-IvanovicZSpasicSSpasojevic-KalimanovskaVTopicAZeljkovicAStefanovicAZunicGEur J Clin Invest20073771572310.1111/j.1365-2362.2007.01849.x17696961Association between intronic SNP in urate-anion exchanger gene, SLC22A12, and serum uric acid levels in JapaneseShimaYTeruyaKOhtaHLife Sci2006792234223710.1016/j.lfs.2006.07.03016920156Change in serum uric acid between baseline and 1-year follow-up and its associated factors in male subjectsChoeJYParkSHKimJYShinIHKimSKClin Rheumatol20082748348910.1007/s10067-007-0732-917874170Ten-year change in serum uric acid and its relation to changes in other metabolic risk factors in young black and white adults: the CARDIA studyRathmannWHaastertBIcksAGianiGRosemanJMEur J Epidemiol20072243944510.1007/s10654-007-9132-317484024Change of glomerular filtration rate in healthy adults with agingSunXChenYChenXWangJXiCLinSLiuXNephrology (Carlton)20091450651310.1111/j.1440-1797.2009.01098.xRelationship of uric acid concentration to cardiovascular risk factors in young men. Role of obesity and central fat distribution. The Verona Young Men Atherosclerosis Risk Factors StudyBonoraETargherGZenereMBSaggianiFCacciatoriVTosiFTraviaDZentiMGBranziPSantiLMuggeoMInt J Obes Relat Metab Disord1996209759808923153Effect of visceral fat accumulation on uric acid metabolism in male obese subjects: visceral fat obesity is linked more closely to overproduction of uric acid than subcutaneous fat obesityMatsuuraFYamashitaSNakamuraTNishidaMNozakiSFunahashiTMatsuzawaYMetabolism19984792993310.1016/S0026-0495(98)90346-89711987Relationship between hyperuricemia and body fat distributionHikitaMOhnoIMoriYIchidaKYokoseTHosoyaTIntern Med2007461353135810.2169/internalmedicine.46.004517827832Relationship between the serum uric acid level, visceral fat accumulation and serum adiponectin concentration in Japanese menTambaSNishizawaHFunahashiTOkauchiYOgawaTNoguchiMFujitaKRyoMKiharaSIwahashiHIntern Med2008471175118010.2169/internalmedicine.47.060318591837Leptin might be a regulator of serum uric acid concentrations in humansBedirATopbasMTanyeriFAlvurMArikNJpn Heart J20034452753610.1536/jhj.44.52712906034Serum leptin is associated with serum uric acid concentrations in humansFruehwald-SchultesBPetersAKernWBeyerJPfutznerAMetabolism19994867768010.1016/S0026-0495(99)90163-410381138Relationship between leptin, insulin, body composition and liver steatosis in non-diabetic moderate drinkers with normal transaminase levelsValtuenaSNumerosoFArdigoDPedrazzoniMFranziniLPiattiPMMontiLZavaroniIEur J Endocrinol200515328329010.1530/eje.1.0196016061835Relationship between resistance to insulin-mediated glucose uptake, urinary uric acid clearance, and plasma uric acid concentrationFacchiniFChenYDHollenbeckCBReavenGMJama19912663008301110.1001/jama.1991.034702100760361820474Relationship between hyperuricemia and metabolic syndromeChenLYZhuWHChenZWDaiHLRenJJChenJHChenLQFangLZJ Zhejiang Univ Sci B20078593598193495617657863Analysis of the relationship between fasting serum uric acid and the insulin sensitivity index in a population-based sample of 380 young healthy CaucasiansClausenJOBorch-JohnsenKIbsenHPedersenOEur J Endocrinol1998138636910.1530/eje.0.13800639461318Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing countryConenDWietlisbachVBovetPShamlayeCRiesenWPaccaudFBurnierMBMC Publ Health20044910.1186/1471-2458-4-9Uric acid and hypertensionSchachterMCurr Pharm Des2005114139414310.2174/13816120577491324616375735Rheumatoid cachexia: a clinical perspectiveSummersGDDeightonCMRennieMJBoothAHRheumatology (Oxford)2008471124113110.1093/rheumatology/ken146Uric acid and inflammatory markersRuggieroCCherubiniABleABosAJMaggioMDixitVDLauretaniFBandinelliSSeninUFerrucciLEur Heart J20062711741181266816316611671Low relative skeletal muscle mass indicative of sarcopenia is associated with elevations in serum uric acid levels: Findings from NHANES IIIBeaversKMBeaversDPSerraMCBowdenRGWilsonRLJournal of Nutrition, Health and Aging20091317718210.1007/s12603-009-0054-5Mechanisms of disuse muscle atrophy: role of oxidative stressPowersSKKavazisANDeRuisseauKCAm J Physiol Regul Integr Comp Physiol2005288R337R34415637170High serum uric acid and low-grade inflammation are associated with smaller LDL and HDL particlesVekicJJelic-IvanovicZSpasojevic-KalimanovskaVMemonLZeljkovicABogavac-StanojevicNSpasicSAtherosclerosis200920323624210.1016/j.atherosclerosis.2008.05.04718603253Uric acid as a mediator of endothelial dysfunction, inflammation, and vascular diseaseKanellisJKangDHSemin Nephrol200525394210.1016/j.semnephrol.2004.09.00715660333Associations between serum uric acid and adipokines, markers of inflammation, and endothelial dysfunctionBoSGambinoRDurazzoMGhioneFMussoGGentileLCassaderMCavallo-PerinPPaganoGJ Endocrinol Invest20083149950418591880Clustering of dyslipidemia, hyperuricemia, diabetes, and hypertension and its association with fasting insulin and central and overall obesity in a general population Atherosclerosis Risk in Communities Study Investigators. SchmidtMIWatsonRLDuncanBBMetcalfPBrancatiFLSharrettARDavisCEHeissGMetabolism19964569970610.1016/S0026-0495(96)90134-18637443The molecular basis for oxidative stress-induced insulin resistanceEvansJLMadduxBAGoldfineIDAntioxid Redox Signal200571040105210.1089/ars.2005.7.104015998259Elevated serum uric acid levels in metabolic syndrome: an active component or an innocent bystander?TsouliSGLiberopoulosENMikhailidisDPAthyrosVGElisafMSMetabolism2006551293130110.1016/j.metabol.2006.05.01316979398The management of goutEmmersonBTN Engl J Med199633444545110.1056/NEJM1996021533407078552148Milestones in the treatment of goutYuTYuTFAm J Med19745667668510.1016/0002-9343(74)90634-24604776Beer, liquor, and wine consumption and serum uric acid level: the Third National Health and Nutrition Examination SurveyChoiHKCurhanGArthritis Rheum2004511023102910.1002/art.2082115593346The influence of dairy products on plasma uric acid in womenGhadirianPShatensteinBVerdyMHametPEur J Epidemiol19951127528110.1007/BF017194317493659Coffee, tea, and caffeine consumption and serum uric acid level: the third national health and nutrition examination surveyChoiHKCurhanGArthritis Rheum20075781682110.1002/art.2276217530681