Dietary, anthropometric, and biochemical determinants of uric acid in free-living adults
Dietary, anthropometric, and biochemical determinants of uric acid in free-living adults
Erick Prado de Oliveira 0 1 3
Fernando Moreto 0 1
Liciana Vaz de Arruda Silveira 2
Roberto Carlos Burini 1
0 Department of Pathology Botucatu School of Medicine (UNESP) , Botucatu , Brazil
1 Center for exercise metabolism and nutrition (CeMENutri) Department of Public Health Botucatu School of Medicine (UNESP) , Botucatu , Brazil
2 Department of Bioestatistic Bioscience Institute (UNESP) , Botucatu , Brazil
3 CeMENutri Departamento de Saude Publica Faculdade de Medicina UNESP , Distrito de Rubiao Jr. s/n, Botucatu, SP 18.618-970 , Brazil
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.
Uric acid; Diet; Body composition; Inflammation; Metabolic syndrome components
Uric acid (UA) is a waste product of the human purine
balance. It is formed by adenosine, inosine,
hypoxanthine, adenine and guanine . Additionally, UA is the
main hydrophilic antioxidant in our organism  as it is
responsible for up to 2/3 of the total antioxidant
capacity in human blood . UA exerts a protective action
on vitamins C and E  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) [7-9].
High urate concentrations are associated with articular
deposits and the onset of (gout) arthritis , metabolic
syndrome (MS) , and with cardiovascular diseases
. It is known that anthropometric parameters,
dyslipidemia, hypertension, inflammation, and insulin
resistance can increase the UA concentration .
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 . 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
Some confounding factors can influence the
hyperuricemia because several variables are associated with each
other [16-19], 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 .
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 . 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.
A descriptive cross-sectional study was conducted in a
subgroup of participants clinically screened for the
lifestyle modification program Mexa-se Pr-Sade [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
midSo Paulo State, approximately 230 km west of the
capital city. It has a population of 121,274 habitants .
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 32722009) 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 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
NutWinW software (2002), version 1.5 , 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 [24-26]. The Healthy Eating Index
(HEI) modified for the Brazilian population was used to
assess the quality of the participants diet . 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
Body weight was measured by a platform-type
anthropometric scale (FilizolaW) with a maximum capacity of
150 kg and an accuracy of 0.1 kg. Height was
determined by a portable SecaW stadiometer with accuracy of
0.1 cm . 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
SannyW anthropometric tape measure (without a lock)
was used for all measurements.
A bioelectrical impedance device (BiodynamicsW,
model 450, USA) was used to determine body fat
percentage (% body fat)  and body-muscle mass, whose data
were used for calculating the muscle-mass index (MMI)
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 . Values of
systolic blood pressure 130 mm Hg and/or diastolic blood
pressure 85 mm Hg were considered to be abnormal
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 (-GT), albumin and total proteins were
performed within 4 hours after blood collection using
the Dry- Chemistry method (VitrosW 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) . Glomerular filtration rate was
calculated  and added in adjustments.
Diagnosis of the metabolic syndrome was performed
according to the NCEP-ATP III criteria [32,33] with
adaptation for glucose values . 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
Operational definition of variables
Higher uric acid was considered when was higher than
the 4th quartile ( > 6,5 mg/dL and > 5 mg/dL).
Overweight was classified as BMI 25 kg/m2 , 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 .
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 . Higher plasma
glucose was defined by 100 mg/dL , higher CRP and
gamma-gt (4th quartile).
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.
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).
As regards dietary intake, greater polyunsaturated fat
intake was observed in individuals with higher UA
quartiles. Smaller intake of vegetables was also observed in
Table 1 Demographic, anthropometric, clinical and laboratory characterization of individuals according to the uric acid
Body Mass ndex (kg/m2)
Muscle Mass ndex (kg/m2)
Table 2 Odds ratio to present the UA at the higher quartile (UA > 6.5 mg/dL and UA > 5 mg/dL) according to
Body Mass Index (abnormal vs normal)
Muscle Mass Index (P25 vs P75)
Waist Circumference (abnormal vs normal)
% body fat (abnormal vs normal)
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.
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).
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
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
Table 3 Odds ratio to present the UA at the higher quartile (UA > 6.5 mg/dL and UA > 5 mg/dL) according to
Carbohydrate ( >60% vs < 50%)
Protein (>15% vs <15%)
Total Lipid (>35% vs 25-35%)
Saturated lipid (>10% vs <10%)
Monounsaturated Lipid (>10% vs <10%)
Polyunsaturated Lipid (>10% vs <10%)
Cholesterol (>300 vs <300 mg)
Fiber (Q4 vs Q1)
Creatinine (abnormal vs normal )
Urea (abnormal vs normal)
Albumin (Q4 vs Q1)
HDL-c (abnormal vs normal)
Gamma-gt (Q4 vs Q1)
Glucose (abnormal vs normal)
LDL-c (abnormal vs normal)
C-Reactive Protein (Q4 vs Q1)
Triglycerides (abnormal vs normal)
Table 4 Odds ratio to present the UA at the higher quartile (UA > 6.5 mg/dL and UA > 5 mg/dL) according to
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.
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).
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).
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 ; 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  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 .
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
Table 5 Analysis of backward stepwise multiple regression of variables according to UA concentrations
Muscle Mass (kg)
Waist Circumference (cm) 0.475
0.638 0.713 0.894 0.372 intercept
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
0.197 0.049 0.016 0.004 4.046 0.000
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 . 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)  and
Fruehwald-Schultes et al., (1999) , 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 .
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
, 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 . 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 . 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 [52-55]. 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 . 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 .
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 , since an association
between UA increase and inflammatory markers has
been reported . 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
. The authors believe that increased urate serum
concentrations would be a causal factor for sarcopenia,
especially through increased inflammation and oxidative stress
. 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 . 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
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 . In-vitro studies showed that UA exerts
a pro-inflammatory effect, thus stimulating the
production of interleukin-1, interleukin-6 the tumor
necrosis factor .
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 . 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 ,
which shows the importance of maintaining urate values
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 , 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
. 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 , 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 .
It is expected that individuals with high UA will have
more chances of presenting MS . MS is associated
with increased oxidative stress , and it known that
UA is a potent antioxidant . Thus, it can be supposed
that urate increase could result from the defense
mechanism from such oxidative stress . 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
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
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
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
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.
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
BMI: Body mass index; MMI: Muscle Mass Index; WC: Waist circumference;
NCEP-ATPIII: National Cholesterol Education Program-Adult Treatment Painel
III; MS: Metabolic Syndrome; GT: -glutamyl transferase; CRP: C-reactive
protein; TG: Triglycerides.
The authors declare that they have no competing interests.
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.
CNPq, CAPES and FAPESP for the financial support and GAP (a statistical
support of Botucatu School of Medicine) for the statistical analysis.
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