Serum Uric Acid and Non-Alcoholic Fatty Liver Disease in
Non-Diabetic Chinese Men
Yuanliang Xie1., Mengjie Wang2., Youjie Zhang1., Shijun Zhang2., Aihua Tan2, Yong Gao2,
Zhengjia Liang3, Deyi Shi3, Zhang Huang3, Haiying Zhang4, Xiaobo Yang4, Zheng Lu1, Chunlei Wu1,
Ming Liao1, Yu Sun5, Xue Qin6, Yanling Hu7, Li Li7, Tao Peng8, Zhixian Li5, Xiaoli Yang7*, Zengnan Mo1,2
1Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China, 2Center for Genomic
and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China, 3Medical Examination Center, Fangchenggang First
People’s Hospital, Fangchenggang, Guangxi Zhuang Autonomous Region, China, 4Department of Occupational Health and Environmental Health at School of Public
Health, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China, 5Department of Ultrasound, First Affiliated Hospital of Guangxi Medical
University, Nanning, Guangxi Zhuang Autonomous Region, China, 6Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning,
Guangxi Zhuang Autonomous Region, China, 7Medical Scientific Research Center, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China,
8Department of Hepatobiliary Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
Increased serum uric acid (SUA) levels may be involved in the development of non-alcoholic fatty liver disease (NAFLD) in
men presenting with metabolic syndrome (MetS) and/or insulin resistance. We aimed to determine the independent
relationship between SUA and NAFLD in non-diabetic Chinese male population, and to explore the determinants of SUA
levels among indexes of adiposity, lipid, and genotypes pertaining to triglycerides metabolism, inflammation, oxidative
stress, and SUA concentrations. A total of 1440 men, classified depending on the presence of ultrasonographically detected
NAFLD, underwent a complete healthy checkup program. Genotypes were extracted from our previously established
genome-wide association study database. After adjusting for age, smoking, drinking, body mass index, homeostasis model
assessment of insulin resistance, C-reactive protein, creatinine, alanine aminotransferase (ALT) and components of
metabolic syndrome, the odds ratio for NAFLD, comparing the highest with the lowest SUA quartile, was 2.81 (95%
confidence interval 1.66–4.76). A stepwise multivariate linear regression analysis (R2=0.238, P,0.001) retained age, waist
circumference, serum creatinine, triglycerides, the Q141K variant in ABCG2 (rs2231142) and NAFLD as significant predictors
of SUA levels (all P,0.001). Besides, ALT and Met196Arg variant in TNFRSF1B (rs1061622) additionally associated with SUA
among individuls with NAFLD. Our data suggest that in Chinese men, elevated SUA is significantly associated with NAFLD,
independent of insulin resistance and other metabolic disorders, such as central obesity or hypertriglyceridemia. Meanwhile,
among subjects with NAFLD, index of liver damage, such as elevated ALT combined with genetic susceptibility to
inflammation associated with increased SUA levels.
Citation: Xie Y, Wang M, Zhang Y, Zhang S, Tan A, et al. (2013) Serum Uric Acid and Non-Alcoholic Fatty Liver Disease in Non-Diabetic Chinese Men. PLoS
ONE 8(7): e67152. doi:10.1371/journal.pone.0067152
Editor: Melania Manco, Scientific Directorate, Bambino Hospital, Italy
Received February 28, 2013; Accepted May 15, 2013; Published July 23, 2013
Copyright: ? 2013 Zhang 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: The work described in this article was supported by grants from the National Natural Science Foundation of China (81060029, 81060234, 30945204,
81060214), Key Program and University Talents Highland Innovation Team of Guangxi (2012012D003, GJR201147-09), Chairman Science and Technology Fund
and Tackle Program of Guangxi (1116-03, GKG1298003-07-01), Guangxi Provincial Department of Finance and Education (2009GJCJ150), and Guangxi Natural
Science Foundation (2011GXNSFA018175, and 2010GXNSFA013133). 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.
* E-mail: email@example.com
. These authors contributed equally to this work.
Nonalcoholic fatty liver disease (NAFLD) comprises a spectrum
of pathologic conditions including simple steatosis, nonalcoholic
steatohepatitis and cirrhosis, influences approximately 20–30% of
the general population and its prevalence is increasing worldwide
. In China, with continually increasing pandemic of metabolic
disorders, such as obesity, insulin resistant and metabolic
syndrome (MetS) , NAFLD has also been emerging at an
alarming rate and posing a very large proportion of the Chinese
population at risk of impending liver diseases in the next decade
[3,4]. NAFLD is commonly associated with obesity and insulin
resistance, which per se are closely related to a cluster of other
metabolic abnormalities, such as hypertriglyceridemia and hyper-
Recently, mounting evidence suggests that elevated serum uric
acid (SUA) frequently associates with the development or
progression of NAFLD [6,7]. Several evidences linking SUA and
NAFLD have been provided from large population based study of
Chinese and American people [8,9]. Li et al found that SUA level
was significantly associated with NAFLD, independ of age, body
mass index (BMI), blood lipids, and fasting plasma glucose .
While another study suggested that elevated SUA level associated
with the development of cirrhosis and increased serum liver
enzymes . Prior epidemiological studies showed that UA is an
independent risk factor for cardiovascular diseases [10,11], and the
PLOS ONE | www.plosone.org1July 2013 | Volume 8 | Issue 7 | e67152
pathological processes included insulin resistance, oxidative
stress, and systemic inflammation [12,13], which are all
considered as important risk factors for the development or
progression of NAFLD . In fact, a recent cross-sectional
analysis of 10732 adults who participated in the National Health
and Nutrition Examination Survey 1988–1994 also demonstrat-
ed that elevated uric acid level is independently associated with
ultrasound-diagnosed NAFLD, regardless of insulin resistance,
components of MetS, and indexes of liver and kidney function
Serum uric acid, balanced between serum uric acid production
and excretion, is the end product of purine metabolism by liver
. Hyperuricemia is a common finding in patients with
metabolic syndrome or its components, such as central obesity
and hypertriglyceridemia . An inverse correlation was also
noted between insulin resistance and decreased renal uric acid
clearance, which is itself associated with elevated SUA . In
addition, increased triglycerides synthesis in individuals with
metabolic abnormalities would also accelerate SUA production
and accumulation . Besides, inflammatory factors, such as
tumor necrosis factor a and it induced oxidative and apoptosis
stress have been suggested to be important factors for more
serious liver damage, resulting in uric acid production. Since
raising evidences suggest that chronic elevation of SUA
concentration would be a causal factor for diseases, such as
metabolic abnormalities and cardiovascular mortality, a well
understanding of factors that influence SUA levels in population
or in NAFLD patients will provide a more accurate interpre-
tation of SUA-NAFLD relationship and has potential implica-
tions on NAFLD treatment in the population. Therefore, the
purpose of the present study is 2-fold: (1) to test the hypothesis
that SUA-NAFLD interrelationship occurs independently from
insulin resistance, MetS, and its components; (2) to explore the
determinants of SUA levels among indexes of adiposity, lipid,
and genotypes pertaining to triglycerides metabolism, inflam-
mation, oxidative stress, and SUA concentrations in a large
series of non-diabetic Chinese men.
Participants and Methods
All subjects, who participated in a large-scale physical exam-
ination from September 2009 to December 2009, were recruited
continuously from the Fangchenggang Area Male Healthy and
Examination Survey (FAMHES). The study has been described
previously in detail . In brief, FAMHES is a population-
based epidemiological cohort study in area of Guangxi, China,
aiming at investigating the effects of environmental and genetic
factors and their interaction on the health of male and the
progress of age-related chronic diseases. After excluded subjects
who currently diagnosed with diabetes mellitus, coronary heart
disease, stroke, hyperthyroidism, rheumatoid arthritis, and
cancer or taking any kind of medication within four weeks, or
with impaired hepatic and renal function, 2426 subjects aged
20–69 years were included. Of those eligible, subjects with
incomplete data involved ultrasonography (n=212), blood test
values (n=342), and genotype of the whole genome (n=83), or
with hepatitis B infection (n=268), alcohol consumption
.40 g/day and .5 times/week (n=52), or C-reactive protein
(CRP) value higher than 10 mg/l (n=29) were further excluded
to avoid bias. Finally, 1440 men with complete data were
included for analyses. All subjects provided written informed
consents, and the study was approved by Ethics and Human
Subject Committee of Guangxi Medical University.
Participants in the FAMHES underwent a detailed medical
interview that included information on demographics, medical
history, smoking status and alcohol consumption. Current smokers
were defined as smoking at least once a day and lasting for more
than six months. Alcohol consumption was defined as consump-
tion of alcoholic drinks (beer, wine, or hard liquor) once or more
per week. Anthropometric parameters, including height, weight,
waist circumference (WC), and blood pressure were measured by
trained personnel using a standardized protocol . BMI was
calculated as weight in kilograms divided by the square of height in
meters. Fasting blood samples were drawn between 8 a.m. and 10
a.m. Serum low-density lipoprotein cholesterol (LDL-C), high-
density lipoprotein cholesterol (HDL-C), triglycerides, fasting
blood glucose (FBG), alanine aminotransferase (ALT), serum
creatinine and SUA were measured using a Dimension-RxL
Chemistry Analyzer (Dade Behring, Newark, DE, USA). Insulin
was measured using COBAS 6000 system E601 electrochemilu-
minescence immunoassay (Roche Diagnostics, IN, Germany), and
high-sensitivity CRP level was detected using the immunoturbidi-
metric assay on the Hitachi 7600 autoanalyzer (Hitachi Corp,
Two experienced ultrasonographers assessed for liver size,
contour, echogenicity, structure and posterior beam attenuation.
Fatty liver was diagnosed based on the findings of abdominal
ultrasonography using a portable ultrasound device (GE, LOGIQ
e, 5.0-MHz transducer, USA) and included the presence of
increased liver echogenicity (bright), and stronger echoes in the
hepatic parenchyma than in the renal parenchyma, vessel blurring
and narrowing of the lumen of the hepatic veins [21,22].
Definition of disease
The diagnosis of NAFLD was based on abdominal ultrasound
without including alcohol consumption, viral, or autoimmune liver
disease . Men with a SUA level .420 mmol/L was defined as
hyperuricemia . Insulin resistance was assessed through the
homeostasis model assessment algorithm using the following
established formulas: glucose (mmol/liter)6Insulin (mlU/liter)/
22.5, and a value of 2.4 or higher was considered insulin resistant
. The metabolic syndrome was diagnosed using the 2005
National Cholesterol Education Program-Adult Treatment Panel
III (NCEP-ATP III) criteria for Asian Americans . The
NCEP-ATP III has defined the metabolic syndrome as the
presence of three or more of the five characteristics of (1) waist
circumference $90 cm; (2) triglycerides $1.7 mmol/L, (3) HDL-
C ,1.03 mmol/L, (4) blood pressure $130/85 mm Hg or current
use of antihypertensive medications, and (5) fasting blood glucose
$5.6 mmol/L or previous diagnosis of type 2 diabetes mellitus or
use of oral antidiabetic agents or insulin.
We extracted several polymorphisms related to lipid metabolism
(rs738409 in PNPLA3) , inflammation (rs1800629 in TNFa,
rs1061622 in TNFRSF1B, and rs8192284 in IL6Ra) and oxidative
stress (rs887829 in UGT1A1 and rs4880 in SOD2) that had been
previously found to be associated with metabolic disorders such as
obesity and NAFLD [28,29,30], and variants involved in uric acid
concentrations (rs2231142 in ABCG2, rs1165205 in SLC17A3,
missense rs16890979 in SLC2A9) [31,32], in our previously
established genome-wide association database . The genotyp-
ing methods have been described previously .
Uric Acid and Non-Alcoholic Fatty Liver Disease
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We classified participants on the basis of quartiles of serum uric
acid, with data presented as mean 6 SE. For descriptive analyses
across the quartile group of SUA, we performed chi-square
analyses for categorical variables and ANOVA for continuous
traits. Logistic regression analyses were use to assess the association
of NAFLD, MetS or its components with SUA, and results were
presented by odds ratio (OR) and 95% confidence intervals (CI). A
multivariate linear regression analysis was used to determine the
effects of anthropometric, clinical, metabolic, and genetic variants
on the logarithm of SUA concentrations. For maintaining the
symmetry and comparability of per-unit-effect estimates, all
models presented use log-transformed values of blood variables.
All statistical analyses were performed with PASW Statistics 18
(Chicago, IL, USA). Statistical tests were 2-sided, and a P
value,0.05 was considered statistically significant.
Among the 1440 participants, the median age of the study was
36 years (interquartile range, 29–44), and median SUA was
374.0 mmol/L (interquartile range, 326.3–426.0). The prevalence
of hyperuricemia, NAFLD and MetS were 27.6% (n=398),
26.3% (n=379) and 14.0% (n=202), respectively. Table 1 shows
the characteristics of study subjects according to quartile of SUA.
Participants with higher serum uric acid concentrations exhibited
higher prevalence of NAFLD and MetS. Meanwhile, BMI, waist
circumference, systolic and diastolic blood pressure, LDL-C,
triglycerides, fasting insulin, HOMA-IR, ALT, and serum
creatinine were significantly higher, while HDL-C was lower,
among men with higher SUA levels.
Association between Serum Uric Acid Concentrations
and NAFLD, MetS or its Components
The ORs for NAFLD increased progressively across the SUA
quartiles (all P,0.001 for trend) (Table 2). After adjusting for age
smoking, and drinking (model 1), the OR for NAFLD, comparing
the highest with the lowest SUA quartile, was 7.51 (95% CI 4.98–
11.31). Further adjustment for BMI (model 2) substantially
attenuated the magnitude of the ORs for NAFLD, but did not
affect statistical significance. Using the lowest SUA quartile as
reference, the ORs for NAFLD was 1.95 (95% CI 1.16–3.31), 3.08
(95% CI 1.85–5.14), and 2.81 (95% CI 1.66–4.76) for quartiles 2,
3, and 4, respectively (P,0.001 for trend), after further adjusting
for HOMA-IR, CRP, creatinine, ALT and components of
metabolic syndrome (model 5).
The ORs for metabolic syndrome substantially increased with
increasing concentrations of SUA (Table 2). Compared with
individuals in the lowest SUA quartile, those in the highest quartile
had an OR of 1.98 (95% CI 1.12–3.50) in the full multivariate
model (model 4). Further adjusted for components of MetS
(without component itself), SUA was positively associated with
hypertriglyceridemia (OR=3.11, 95% CI 2.03–4.77) (P,0.001
for trend), and central obesity (OR=2.01, 95% CI 1.00–4.03)
with a borderline statistical significance (P=0.05).
We also duplicated our analysis among subgroups with or
without MetS, central obesity, or hypertriglyceridemia (Figure 1).
In general, the risks of NAFLD were more pronounced among
subjects with higher concentration of SUA and with combination
Table 1. Characteristics of participants according to quartile (Q) of serum uric acid (n=1440).
Serum uric acid quartile
Variable Q1 (n=360)Q2 (n=363)Q3 (n=366) Q4 (n=351)
Uric acid (mmol/L)288.361.8 350.660.7 400.260.8 490.463.0
Age (years)38.860.637.260.637.060.6 37.560.6 0.113
Current smoker (n, %)177 (49.2) 184 (50.7)186 (50.8)170 (48.4) 0.899
Alcohol drinker (n, %) 297 (82.5)312 (86.0)317(86.6)305 (86.9)0.31
NAFLD (n, %)36 (10.0) 71 (19.6) 120 (32.8)152 (43.3)
BMI (kg/m2) 22.260.122.960.2 23.660.2 24.960.2
Waist circumference (cm) 77.760.479.560.5 81.560.585.360.5
Systolic blood pressure (mmHg)117.660.8117.060.7 118.360.8121.060.80.003
Diastolic blood pressure (mmHg)75.760.575.660.577.760.579.560.6
Metabolic syndrome (n, %)25 (6.9) 35 (9.6)52 (14.2)90 (25.6)
Insulin (mlU/L)6.660.3 7.218.104.22.1680.460.5
C-reactive protein (mg/L) 0.9360.080.9660.071.0360.071.1560.070.152
Data are means 6 SE or raw numbers (%). Continuous data were used for univariate general linear models and categorical data were analyzed by x2tests.
Abbreviation: ALT, alanine aminotransferase; BMI, body mass index; LDL-C, serum low-density lipoprotein cholesterol; HDL-C, serum high-density lipoprotein
cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; NAFLD, nonalcoholic fatty liver disease.
Uric Acid and Non-Alcoholic Fatty Liver Disease
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of metabolic disorders. Among the subjects without metabolic
syndrome (Figure 1A), compared with the lowest serum uric acid
quartile (reference group), those in the highest quartile had an OR
of 2.50 (95% CI 1.43–4.36) for NAFLD. The OR increased
noticeably with the combination of MetS and high serum uric acid
concentrations with an OR of 7.18 (95% CI 3.42–15.08) for
NAFLD. No significant interaction was observed between serum
uric acid and MetS (P for interaction=0.36). In addition,
participants with central obesity (Figure 1B) and hypertriglycer-
idemia (Figure 1C), compared with the reference group, those in
the highest quartile had ORs of 4.22 (95% CI 2.05–8.67) and 7.16
(95% CI 3.74–13.72) for NAFLD respectively. No significant
interaction was observed between NAFLD and central obesity,
and hypertriglyceridemia (P for interaction=0.22, and 0.94
Factors Associated With Serum Uric Acid Concentrations
Ln-normalized SUA level was introduced as a dependent
variable in the multivariate linear regression models (Figure 2),
using age, BMI, waist circumference, NAFLD and MetS (both
classified as yes or no), and log-transformed values of triglycerides,
CRP, HOMA-IR, creatinine, and ALT, and the genomic variants
as independent variable. Overall, this model explained 23.5% of
the variability in logarithm of SUA concentrations. Among all
subjects, a final constructed model using a stepwise method
(probability to enter #0.05; to remove $0.10), found age
(b=20.11, 95% CI 20.16 to 20.06), WC (b=0.17, 95% CI
0.11–0.23), NAFLD (b=0.15, 95% CI 0.09–0.21), log-trans-
formed serum creatinine (b=0.29, 95% CI, 0.24–0.34) and
triglycerides (b=0.11, 95% CI 0.05–0.16), as well as the Q141K
variant in ABCG2 gene (b=0.12, 95% CI 0.07–0.17) as significant
predictors (all P,0.001) of the logarithm of SUA levels
(R2=0.238, P,0.001). When duplicated our stepwise regression
analysis among subjects with NAFLD, interestingly, the WC
(b=0.21, 95% CI 0.09–0.33, P=0.001), log-transformed triglyc-
erides (b=0.11, 95% CI 0.02–0.20, P=0.02), creatinine (b=0.19,
95% CI 0.10–0.28, P,0.001), and ALT (b=0.14, 95% CI 0.05–
0.24, P=0.003), and the Met196Arg variant in TNFRSF1B gene
(b=0.10, 95% CI 0.01–0.20, P=0.027) were positively associated
with logarithm of SUA concentrations (R2=0.148, P,0.001).
We observed a strong positive association between elevated
serum uric acid levels and the risks of NAFLD in non-diabetic
Chinese men, independent of insulin resistance or metabolic
syndrome status. Our data implied the crucial role of SUA as an
important independent risk factor for NAFLD. In addition, we
identified a missense SNP in ABCG2 gene (rs2231142) associated
with SUA level, further clustered with independent variables, such
as age, waist circumference, NAFLD, creatinine and triglycerides,
which explained 23.8% of the variability in the logarithm of serum
uric acid concentration. Besides, ALT and variant Met196Arg of
TNFRSF1B gene (rs1061622) combined with WC, creatinine, and
triglycerides, associated with SUA among subjects with NAFLD.
Table 2. Odds ratios and 95% confidence interval for NAFLD, metabolic syndrome, and its components according to quartile (Q)
of serum uric acid.
Serum uric acid
Q1Q2 Q3 Q4
P for trend
Model 11.002.34 (1.51–3.61)4.79 (3.17–7.24)7.51 (4.98–11.31)
Model 2 1.001.84 (1.13–3.02) 3.17 (1.99–5.07)3.35 (2.10–5.34)
Model 31.001.85 (1.13–3.03)3.16 (1.98–5.06) 3.30 (2.07–5.28)
Model 41.00 1.85 (1.11–3.07) 3.17 (1.94–5.17)3.32 (2.01–5.49)
Model 5 1.001.95 (1.16–3.31)3.08 (1.85–5.14) 2.81 (1.66–4.76)
Model 11.001.57 (0.91–2.70)2.49 (1.49–4.14) 5.32 (3.28–8.62)
Model 21.001.05 (0.58–1.92) 1.32 (0.75–2.31)2.01 (1.17–3.44)0.019
Model 31.001.06 (0.58–1.94)1.31 (0.74–2.30)2.10 (1.22–3.63)0.012
Model 41.001.03 (0.56–1.89)1.26 (0.71–2.25)1.98 (1.12–3.50) 0.031
Components of metabolic syndrome*
Central obesity1.001.03 (0.49–2.15)1.43 (0.72–2.86)2.01 (1.00–4.03)0.101
Hypertriglyceridemia 1.001.51 (0.99–2.31)1.74 (1.15–2.65) 3.11 (2.03–4.77)
Elevated BP1.001.09 (0.76–1.56) 1.23 (0.86–1.77)1.09 (0.74–1.59)0.703
Low HDL cholesterol1.000.48 (0.25–0.93)0.72 (0.39–1.33)0.64 (0.34–1.21)0.181
Hyperglycemia1.000.85 (0.59–1.22) 0.73 (0.50–1.06) 0.77 (0.52–1.15)0.402
Model 1 was adjusted for age smoking, and drinking;
Model 2 was further adjusted for BMI;
Model 3 was further adjusted for HOMA-IR and C-reactive protein;
Model 4 was further adjusted for serum creatinine and alanine aminotransferase;
Model 5 was further adjusted for the components of metabolic syndrome (variables as categories).
*Fully adjusted model without component itself.
Serum levels of HOMA-IR, C-reactive protein, creatinine and alanine aminotransferase were log transformed.
Abbreviation: BP, blood pressure; NAFLD, nonalcoholic fatty liver disease.
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Our findings are partly consistent with and extend an earlier
Chinese cross-sectional study , which reported lower prevalence
of hyperuricemia (14.7% vs. 27.6%) and NAFLD (11.8% vs.
26.3%) than our results. A possible explanation for the different
prevalence may originate from the characteristics of the studied
populations. In the present study, Fangchenggang people living in
coastal areas have a relatively frequent seafood diet, which are
closely related with elevated serum uric acid , and possibly
influence the natural characteristics of NAFLD.
Studies have consistently shown an association between elevated
SUA and risk of NAFLD, and are in accord with prior hypotheses
suggesting that SUA might be an important contributor to the
development of NAFLD. In studies of 8925 employees of Ningbo
province in China, hyperuricemia was related to NAFLD,
independently of metabolic risk factors at baseline, and after a
3-year follow-up, SUA levels were independently and positively
associated with the risk for incident NAFLD, although insulin
resistance was not considered [6,8]. Another prospective study
among healthy Korean men also found SUA appeared to be an
independent predictor for developing ultrasonographically detect-
ed NAFLD; the investigators did not measure the waist
circumference, which may be a better surrogate marker of central
obesity . Our data combined with previous findings suggested
that higher levels of SUA are commonly associated with metabolic
syndrome and its five components, especially central obesity and
hypertriglyceridemia , which are tightly related to NAFLD
. In addition, we also noted a strong positive association
between higher levels of SUA and the risks of NAFLD,
independent of indexs of obesity, insulin resistance, MetS, and
liver and kidney function. Although SUA increase is also observed
in individuals with insulin resistance, we found that the increased
risks for NAFLD by hyperuricemia could not be explained merely
through peripheral HOMA-IR. The possible explanation is that
SUA increase is individuals with insulin resistance, probably
because hyperinsulinemia would cause lower renal UA excretion
, and indirect act on SUA via reduction of adipocyte sensitivity
to insulin and then increases triglyceride lipolysis within adipose
Metabolic and renal factors and genetic variation might
contribute to determining uric acid concentration through
regulation of uric acid synthesis, excretion, or reabsorption .
Because whether SUA as a marker or a cause or both, strategies
that aim at monitoring or decreasing SUA levels may have clinical
beneficial effects to prevent or reduce the risk of NAFLD. Our
present result also suggested that the Q141K variant in ABCG2,
leads to variable degree of SUA concentration in men, in
conceptual agreement with the ABCG2’s function of altering uric
acid transport in kidney proximal tubule cell and excretion in liver
via the biliary system . However, more importantly, the
relatively strong association of increased SUA levels with NAFLD
raises the possibility that SUA overload might play some
pathogenic role in the development of NAFLD, given that
progressive SUA accumulation contributes to inflammatory and
oxidative effects [12,13]. On the contrary, these levels are not
influenced by global obesity, MetS, CRP, HOMA-IR, ALT or by
genomic variants related to PNPLA3, chronic inflammation, and
oxidative stress in the overall population.
Recently, evidence revealed that uric acid, released from injured
cells, induced sterile inflammation [38,39]. Small molecules like
ATP or large crystals like UA can be transformed or exported
from the liver under normal physiological condition. However,
non-alcoholic steatohepatitis, one of important component of liver
damage, might induce cell death results in the release and
accumulation of molecules are not present in the extracellular
environment during health, such as UA . Thus, our results of
positive association of elevated serum ALT and the Met196Arg
variant in TNFRSF1B with SUA concentrations among subjects
with NAFLD further support this proposal, which is called damage
associated molecular patterns (DAMPs), and suggested that the
release of UA might be accelerated when tissue injury (combined
with a genetic susceptibility to inflammation) happened in NAFLD
patients . Then UA as a promising production of DAMPs
triggers sterile inflammation and increases organ damage, based
on prior hypothesis role of inducing inflammation and oxidative
Figure 1. Odds ratios (OR) and 95% confidence interval (CI) for
NAFLD. Adjusted for age, smoking, drinking, BMI, HOMA-IR, C-reactive
protein, creatinine and alanine aminotransferase The black and white
circles are the ORs for NAFLD among subjects with or without MetS (A),
central obesity (B), and hypertriglyceridemia (C) respectively. The error
bars indicate the 95% CI of OR, and broken lines indicate the OR=1.
Serum levels of HOMA-IR, C-reactive protein, creatinine and alanine
aminotransferase were log transformed.
Uric Acid and Non-Alcoholic Fatty Liver Disease
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We simultaneously investigate the effects of metabolic syndrome
and its components, and insulin resistance on the relationship
between SUA and NAFLD in a large population-based sample.
And to our knowledge, we first explore the determinants of SUA
levels among multiple variables and genotypes in Chinese men.
However, several potential limitations are admitted. First, NAFLD
diagnosis is based on ultrasound imaging, which is neither sensitive
enough to distinguish hepatic steatosis from NASH, nor to
distinguish the stage of hepatic fibrosis only in the case that
cirrhosis is present. However, ultrasonographic examination
currently remains the primary method for epidemiologic studies
of NAFLD owing to its non-invasiveness, safety, wide availability
and convenience. Second, given the nature of cross-sectional
study, whether elevated SUA is a cause or an effect of NAFLD
cannot be answered accurately. Furthermore, a recent study
showed that hyperuricaemia was independently associated with
severity of steatosis among chronic hepatitis C patients; therefore,
another potential limitation might be including patients with
Figure 2. Multiple linear regression analysis of the logarithm of serum uric acid. The squares are the standardized regression coefficients
(b) and the error bars indicate the 95% CI of b, and broken lines indicate the b coefficients=0. Genomic variants were coded as dummy variables: 0
for homozygosity for wild-type alleles, 1 for heterozygosity, and 2 for homozygosity for effect alleles.
Uric Acid and Non-Alcoholic Fatty Liver Disease
PLOS ONE | www.plosone.org6 July 2013 | Volume 8 | Issue 7 | e67152
hepatitis C virus infection, due to the absence of diagnostic Download full-text
markers in our study.
In conclusion, elevated serum uric acid is independently
associated with NAFLD regardless of insulin resistance and
metabolic syndrome status, especially hypertriglyceridemia or
central obesity. SUA are interrelated with age, waist circum-
ference, NAFLD, creatinine, triglycerides, and the Q141K
variant in ABCG2 in non-diabetic Chinese men. Meanwhile,
among subjects with NAFLD, index of liver damage, such as
elevated ALT combined with genetic susceptibility to inflam-
mation (Met196Arg variant in TNFRSF1B) associated with
increased SUA levels. Strategies that aim at modulating the
SUA levels and/or improving liver function may have
significant clinical implications for the prevention and treat-
ment of NAFLD.
Conceived and designed the experiments: ZM. Performed the experiments:
YX MW YZ Z. Lu CW ML YS. Analyzed the data: Xiaoli Yang MW YH.
Contributed reagents/materials/analysis tools: SZ ZH XQ AT YG. Wrote
the paper: MW JY. Coordinated the project: Z. Liang, DS HZ Xiaobo
Yang LL TP Z. Li.
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