International Journal of Endocrinology

International Journal of Endocrinology / 2017 / Article

Clinical Study | Open Access

Volume 2017 |Article ID 5438157 | https://doi.org/10.1155/2017/5438157

Cuiling Zhu, Ran Cui, Mingming Gao, Sharvan Rampersad, Hui You, Chunjun Sheng, Peng Yang, Hui Sheng, Xiaoyun Cheng, Le Bu, Shen Qu, "The Associations of Serum Uric Acid with Obesity-Related Acanthosis nigricans and Related Metabolic Indices", International Journal of Endocrinology, vol. 2017, Article ID 5438157, 9 pages, 2017. https://doi.org/10.1155/2017/5438157

The Associations of Serum Uric Acid with Obesity-Related Acanthosis nigricans and Related Metabolic Indices

Academic Editor: Andre Pascal Kengne
Received20 Jul 2016
Revised08 Nov 2016
Accepted27 Nov 2016
Published07 Mar 2017

Abstract

Objective. Recent studies have shown that hyperuricemia (HUA) is associated with hypertension, dyslipidemia, insulin resistance, and metabolic syndrome (MetS). We aimed to examine the relationship of serum UA with Acanthosis nigricans (AN) and related metabolic indices in obese patients. Methods. A cross-sectional study with 411 obese patients recruited from our department was analyzed in this study. Weight, body mass index (BMI), UA, lipid profile, liver function, and renal function were measured in all participants. Oral glucose tolerance tests were performed, and serum glucose, insulin, and C peptide were measured at 0, 30, 60, 120, and 180 min. Results. AN group had higher serum UA levels than OB group. Circulating UA levels were associated with BMI, dyslipidemia, hypertension, IR, and AN. In logistic regression analyses (multivariable‐adjusted), a high serum UA level was associated with high odds ratios (ORs) (95% confidence interval [CI]) for AN in females (ORs = 3.00 and 95% CI [1.02–8.84]) and males (ORs = 6.07 and 95% CI [2.16–17.06]) in the highest quartile (Q4) of serum UA. Conclusions. Serum UA levels were positively associated with multiple metabolic abnormalities including obesity, hypertension, hyperglycemia, hyperlipidemia, and AN and may be an important risk factor in the development of AN; further evidences in vitro and in vivo are needed to investigate the direct or indirect relationship.

1. Introduction

Uric acid (UA) is the end-product of purine metabolism in humans [1]. In the last few decades, the prevalence of hyperuricemia (HUA) has been rapidly increasing worldwide [2, 3]. HUA has been traditionally considered to be a risk factor for hypertension, diabetes mellitus, cardiovascular disease, renal disease, and metabolic syndrome (MetS) [47]. Growing epidemiological studies suggested that serum UA levels may predict the development of MetS. In the study by Lin et al., serum UA levels were elevated significantly as the number of metabolic components increased [8]. In a study in Chinese, the prevalence of MetS increased with rise in serum UA levels and MetS component number presented a significantly increasing trend across serum UA quartiles in both sexes. Additionally, participants with HUA or higher serum UA levels were at significantly higher ORs for MetS and its related components including abdominal obesity, hypertension, hyperglycemia, and low HDL cholesterol [9, 10]. These previous studies suggested that serum UA levels may be a useful predictor for metabolic disorders.

AN is a typical skin lesion characterized by velvety, brownish-black, papillose thickening hyperpigmentation of the skin of the epidermis [1113]. Clinical studies have shown that obesity-related AN is usually accompanied by metabolic disorders, including overweight, abnormal glucose metabolism, dyslipidemia, and fatty liver [12, 14]. The occurrence of obesity-related AN is significantly related with insulin resistance and hyperinsulinemia [15, 16]. However, in some cases, patients such as obesity or type 2 diabetes with significant insulin resistance do not have AN. This indicates that insulin resistance is not the only predominant factor in the pathophysiological process of AN.

AN, as a disorganized metabolic state, might be predicted by serum UA. However, the role of serum UA level in the development of obesity-related AN is not yet understood. The present study aimed to investigate the relationship of serum UA, AN, and related metabolic indices in obese patients.

2. Methods

2.1. Study Design

We conducted a cross-sectional study with 411 obese patients recruited from our outpatient and inpatient department from July 2015 to March 2016. They are divided into two groups including 220 obese patients without AN (OB group) and 191 obese patients with AN (AN group). The study protocol was approved by the Hospital Research Ethics Review Committee, and written informed consent was obtained from all participants (Clinical Trials Registration Number is ChiCTROCS-12002381, http://www.who.int/ictrp).

2.2. Study Subjects

A total of 411 obesity participants (52.0% females and 48.0% males) were consecutively enrolled in this study. Inclusion criteria: obesity in this study was defined as BMI ≥ 28 kg/m2 according to the diagnostic criteria for obesity in a Chinese population [17]. Exclusion criteria included the presence of malignant tumor, renal dysfunction, severe liver dysfunction (aspartate aminotransferase (AST) or alanine aminotransferase (ALT) levels more than 2.5 times the normal value), and a history of preexisting heart disease. In addition, those who were treated using any medication or other therapeutic methods that could influence the weight, glucose metabolism, lipid metabolism, or uric acid levels, such as hypoglycemic agents, lipid-lowering, and uric acid-lowering agents (allopurinol or benzbromarone) in the 3-month period prior to this study had been excluded in this study.

2.3. Definitions of Acanthosis nigricans

AN is an easily identifiable skin condition that is strongly associated with insulin resistance, characterized by velvety, brownish-black pigmentation of the skin folds, mainly found in the posterior aspect of the neck, axillae, elbows, knees, umbilicus, and occasionally mucosal surfaces [1115]. A quantitative scale of AN has been developed by Burke et al. [14], 0—absent: not detectable on close inspection; 1—present: clearly present on close visual inspection, not visible to the casual observer, extent not measurable; 2—mild: limited to the base of the skull, does not extend to the lateral margins of the neck (usually <3 inches in breadth); 3—moderate: extending to the lateral margins of the neck (posterior border of the sternocleidomastoid, usually 3–6 inches), should not be visible when the participant is viewed from the front; 4—severe: extending anteriorly (>6 inches), visible when the participant is viewed from the front.

2.4. Measurements

All the patients underwent a physical examination (including measurements of height, weight, waist circumference (WC), hip circumference (HC), systolic blood pressure (SBP), diastolic blood pressure (DBP), and percentage of body fat (%)). Signs of Acanthosis nigricans were assessed by a trained physician. Routine blood biochemical tests, including serum UA, blood creatinine (Cr), liver function, blood glucose, and blood lipids, were performed. The liver function tests included aspartate aminotransferase (AST) and alanine aminotransferase (ALT). The blood lipid tests included total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). The oral glucose tolerance tests were performed, and the insulin and C peptide levels were measured at 0, 30, 60, 120, and 180 min, among which height and weight were measured by a simple anthropometric measuring instrument (Omron HBF-358, Japan) with patients lightly clothed and without shoes in a standing position. Body fat (%) was measured by dual DEXA. SBP and DBP were measured twice in the right arm of subjects who had been resting for at least 10 min in a seated position using a mercury sphygmomanometer, and BMI was calculated for all participants. WC and HC were made using an unstretched tape without any pressure to the body surface. Blood samples were taken from each subject after an overnight fast. The participants were divided into four quartiles (Q1, Q2, Q3, and Q4) according to the serum UA levels and gender.

BMI was calculated as weight in kilograms divided by the square of height in meters. Insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR) [18], which was calculated as fasting plasma glucose (FPG) (mmol/L) × fasting insulin (FINS) (mU/L)/22.5. Insulin sensitivity index (ISI) was calculated as 1/FINS (mU/L) × FPG (mmol/L), which was used to assess the insulin sensitivity.

2.5. Statistical Analysis

All statistical analyses were performed using the SPSS 20.0 package. Means and standard deviations (mean ± SD) or medians (interquartile range) were calculated for continuous variables. Non-normally distributed data were logarithmically transformed to normality (HOMA-IR), when needed. Comparisons between groups were tested using Student’s t-test or analysis of variance (ANOVA), and least significant differences (LSD) post hoc tests, when the data is normally distributed. Non-normally distributed data were analyzed by nonparametric test. Linear regression analyses were used to estimate the trends of continuous variables, and chi-square tests for trends in proportions were performed for categorical variables across the increasing subgroup-specific quartiles of serum UA. Repeated measure ANOVA was used for comparing glucose, insulin, and C peptide level at 0, 30, 60, 120, and 180 min among the four UA groups. Analyses of covariance were performed to estimate the associations between serum UA, HOMA-IR, and ISI in unadjusted and multivariable-adjusted models. To examine associations between serum UA level and AN, we ran three logistic regression models: (1) for age and BMI; (2) for age, BMI, FPG, and LDL; and (3) for HOMA-IR in addition to all covariates in (2) both in females and males. ORs and corresponding 95% CI were calculated. Figures in this study were produced by GraphPad Prism 5 project. All reported values are two-sided and considered statistically significant at <0.05.

3. Results

3.1. General Characteristics

The clinical and metabolic characteristics of the study population are presented in Table 1. Patients in AN group have more severe metabolic disorders including obesity, hypertension, glucose metabolic disorder, hyperlipidemia, and insulin resistance. The serum UA levels in AN group were found to significantly increase compared with OB group in both men and women. Similarly, weight, BMI, WC, SBP, ALT, and AST were significantly higher in AN group than OB group for both sexes. In addition, height, WHR, DBP, and percentage of body fat increased significantly in females. As for lipid metabolism aspect, the HDL levels in AN group were found to significantly decrease in males, while no significant difference in the levels of TC, TG, and LDL was observed compared with OB group. As for glucose metabolism, there was no significant difference at 0 min glucose levels in either gender. However, patients in AN group showed significantly higher insulin and C peptide levels at 0 min compared with the OB group among females and males. Furthermore, the intransformed HOMA-IR levels significantly increased in AN group for all participants.


ParametersFemale ()Male ()
OB ()AN ()OB ()AN ()

UA (μmol/L)344.2 ± 65.1407.8 ± 89.1<0.001430.6 ± 82.4496.3 ± 112.5<0.001
Age (yr)31 (12)26 (12)<0.00130 (10)24 (14)<0.001
Weight (kg)81.77 ± 11.4395.15 ± 15.23<0.001106.52 ± 14.79114.42 ± 20.040.002
BMI (kg/m2)30.99 ± 3.5235.10 ± 5.04<0.00134.29 ± 3.9636.79 ± 5.220.002
Waist circumference (cm)99.46 ± 9.49109.99 ± 12.62<0.001112.51 ± 10.46117.03 ± 11.630.011
Waist/hip ratio0.916 ± 0.0690.960 ± 0.062<0.0010.987 ± 0.0590.998 ± 0.0440.137
Percentage of body fat (%)36.98 ± 3.4138.68 ± 4.380.00231.88 ± 5.2132.72 ± 4.990.940
SBP (mmHg)128 ± 17135 ± 150.006133 ± 14140 ± 140.001
DBP (mmHg)83 ± 1087 ± 110.01483 ± 1185 ± 110.305
Cr (μmol/L)56.88 ± 7.3656.82 ± 8.880.96474.95 ± 13.7174.28 ± 13.290.760
ALT (U/L)32.72 ± 26.9756.96 ± 41.55<0.00161.73 ± 56.2185.08 ± 58.03<0.001
AST (U/L)25.19 ± 14.0634.77 ± 20.29<0.00133.92 ± 27.9643.84 ± 24.35<0.001
TC (mmol/L)5.02 ± 1.124.86 ± 0.930.4734.93 ± 0.954.97 ± 1.000.774
TG (mmol/L)1.64 ± 0.992.03 ± 2.490.3992.02 ± 1.171.79 ± 0.830.207
HDL (mmol/L)1.16 ± 0.261.13 ± 0.490.0561.04 ± 0.190.97 ± 0.190.015
LDL (mmol/L)3.08 ± 1.012.97 ± 0.860.6503.09 ± 0.833.22 ± 0.850.307
BG0 (mmol/L)5.41 ± 1.095.77 ± 1.700.0795.81 ± 2.195.38 ± 1.100.471
INS0 (mU/L)23.91 ± 21.4034.12 ± 17.91<0.00125.64 ± 18.4839.66 ± 31.74<0.001
CP0 (ng/mL)3.37 ± 1.284.42 ± 1.30<0.0013.68 ± 1.434.75 ± 1.82<0.001
HOMA-IRa1.54 ± 0.642.01 ± 0.61<0.0011.67 ± 0.702.04 ± 0.65<0.001

Data are expressed as mean ± SD or median (interquartile range); versus OB, ; ; ; athe data was ln-transformed to normality before analysis. AN: obese group with Acanthosis nigricans; OB: simple obese group; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; UA: uric acid; Cr: serum creatinine; ALT: alanine transaminase; AST: aspertate aminotransferase; TC: total cholesterol; TG: triglyceride; HDL: high-density lipoprotein; LDL: low-density lipoprotein; BG0: 0 min glucose; INS0: 0 min insulin; CP0: 0 min C peptide; HOMA-IR: homeostasis assessment model of insulin resistance.
3.2. Relationship between Serum UA and Metabolic Indices

Due to the significant difference in serum UA concentrations between females and males, we divided subjects into four gender-specific quartiles (Q1, Q2, Q3, and Q4) according to the serum UA levels as follows: among females, Q1, <310 μmol/L; Q2, 311~360 μmol/L; Q3, 361~424 μmol/L; Q4, ≥425 μmol/L; among males, Q1, <393 μmol/L; Q2, 394~458 μmol/L; Q3, 459~528 μmol/L; Q4, ≥529 μmol/L. We analyzed the mean value of each characteristic and metabolic index for each serum UA quartile (Table 2). We found that BMI, WC, and HC significantly increased from the lowest quartile of serum UA (Q1) to the highest serum UA quartile (Q4) for both sexes. As for blood pressure and lipid profiles, there was different demonstration in different genders. In females, SBP and DBP had significantly positive association with serum UA levels, while, in males, SBP had a significantly positive association with serum UA levels, but DBP had a nonsignificant association with serum UA. In the items of lipid profiles, the levels of TC, TG, and LDL were positively related with serum UA, while HDL levels were negatively related with serum UA in females. In the males, the similar relationship between HDL and UA was observed, while the relationship between other lipid profiles and UA was not significant. As for glucose metabolism (shown in Figure 1), no significant difference was observed in glucose levels between quartiles at any point in either gender (Figures 1(a) and 1(d)). However, among females, significant difference was observed in insulin and C peptide levels across the serum UA quartiles (, ) (Figures 1(b) and 1(c)). Similarly, among males, significant difference was observed in C peptide levels across the serum UA quartiles (Figure 1(f)), whereas no significant difference was observed in insulin levels across the quartiles (Figure 1(e)). As was shown in Figure 2, before adjusting for potential confounders, HOMA-IR index was found to significantly increase across the serum UA quartiles in females, while no increasing trend was observed in males (Figure 2(a)). Similarly, ISI was found to significantly decrease across the serum UA quartiles in females, and no decreasing trend was observed in males (Figure 2(c)). An analysis of covariance was performed to investigate the association between serum UA levels and HOMA-IR and ISI after controlling for confounders. After adjusting age, SBP, DBP, AST, and WHR, the HOMA-IR was found to have a significantly increasing trend and ISI have a significantly decreasing trend across the quartiles in females while no significant difference across the quartiles was observed in males (Figures 2(b) and 2(d)).


ParametersQuartiles of serum UA in females for trend
Q1 (<310)Q2 (311–360)Q3 (361–424)Q4 (≥425)

n54535454
Age (years)33 (19)29 (12)28 (11)26 (12)0.001
BMI (kg/m2)31.54 ± 4.6431.20 ± 4.0632.74 ± 4.2034.58 ± 4.77<0.001
Waist circumference (cm)100.96 ± 13.45101.63 ± 12.04103.91 ± 10.18106.84 ± 11.01<0.001
Hip circumference (cm)107.89 ± 10.45109.49 ± 8.62111.87 ± 9.00113.68 ± 9.57<0.001
SBP (mmHg)129.1 ± 15.9129.6 ± 18.8131.3 ± 15.6133.3 ± 16.9<0.001
DBP (mmHg)82.4 ± 9.383.5 ± 12.286.4 ± 12.587.4 ± 10.7<0.001
TC (mmol/L)4.84 ± 0.994.99 ± 1.254.92 ± 0.995.13 ± 0.980.010
TG (mmol/L)1.69 ± 1.061.43 ± 0.761.66 ± 1.022.35 ± 2.95<0.001
HDL (mmol/L)1.12 ± 0.261.15 ± 0.201.15 ± 0.281.16 ± 0.570.001
LDL (mmol/L)2.99 ± 0.893.18 ± 1.052.98 ± 0.933.03 ± 0.980.040
Acanthosis nigricans (%)25.922.633.366.7<0.001

ParametersQuartiles of serum UA in males for trend
Q1 (<393)Q2 (394–458)Q3 (459–528)Q4 (≥529)

n49495048
Age (years)28 (13)30 (13)27 (12)23 (15)0.098
BMI (kg/m2)34.39 ± 4.8135.02 ± 4.2236.32 ± 4.5737.12 ± 5.48<0.001
Waist circumference (cm)112.48 ± 12.87113.28 ± 9.58115.41 ± 9.88119.19 ± 11.86<0.001
Hip circumference (cm)114.26 ± 9.22113.10 ± 7.04115.69 ± 8.69120.46 ± 11.42<0.001
SBP (mmHg)135.4 ± 14.2135.2 ± 14.5135.4 ± 14.1142.1 ± 15.80.005
DBP (mmHg)83.9 ± 10.385.0 ± 10.985.2 ± 10.784.4 ± 12.80.912
TC (mmol/L)4.72 ± 0.835.18 ± 1.115.06 ± 1.034.86 ± 0.870.359
TG (mmol/L)1.67 ± 0.931.92 ± 1.032.04 ± 1.051.95 ± 0.970.256
HDL (mmol/L)1.05 ± 0.191.00 ± 0.161.02 ± 0.230.95 ± 0.190.005
LDL (mmol/L)2.95 ± 0.693.37 ± 0.963.23 ± 0.893.09 ± 0.760.186
Acanthosis nigricans (%)38.859.25079.2<0.001

value by linear regression analysis and chi-square tests for trends in proportions. ; ; .
3.3. Relationship of Serum UA Levels with the Occurrence of AN

In this study, we used the chi-square tests for trends to compare the prevalence of AN for each quartile among females and males (Table 2). The results showed that the prevalence of AN in Q4 was 66.7% among females and 79.2% among males and was significantly higher compared to Q1 for both men and women (, , resp.). In logistic regression models, serum UA levels were significantly associated with the prevalence of AN in Q4 in both sexes and the ORs (95% CI) for AN in Q4 were 5.71 (95% CI, 2.49–13.12) among female participants and 6.00 (95% CI, 2.43–14.80) among male participants (Figure 3). Additionally, after adjusting for potential confounders (age and BMI involved in Model 1; age, BMI, FPG, and LDL involved in Model 2; age, BMI, FPG, LDL, and HOMA-IR involved in Model 3), serum UA was still significantly associated with high ORs (95% CI) for AN in both sexes (Table 3). Compared with Q1, among the female participants, the ORs (95% CI) for AN in Q4 were 2.87 (1.10–7.46) in Model 1, 2.91 (1.07–7.91) in Model 2, and 3.00 (1.02–8.84) in Model 3. Among the male participants, the ORs (95% CI) for AN in Q4 were 5.04 (1.88–13.53) in Model 1, 5.67 (2.04–15.75) in Model 2, and 6.07 (2.16–17.07) in Model 3. Moreover, the ORs and 95% CI for AN in male participants in Q2 were significantly higher than those in Q1 in three models.


ModelsIndependent variablesOR (95% CI)Independent variablesOR (95% CI)
FemaleMale

Model 1Q10.0171.00 (reference)Q10.0051.00 (reference)
Q20.6030.77 (0.28–2.09)Q20.0143.07 (1.25–7.53)
Q30.7820.87 (0.33–2.28)Q30.3691.48 (0.63–3.50)
Q40.0312.87 (1.10–7.46)Q40.0015.04 (1.88–13.53)

Model 2Q10.0361.00 (reference)Q10.0041.00 (reference)
Q20.8230.89 (0.31–2.54)Q20.0173.22 (1.23–8.42)
Q30.9090.94 (0.35–2.54)Q30.4131.47 (0.59–3.66)
Q40.0362.91 (1.07–7.91)Q40.0015.67 (2.04–15.75)

Model 3Q10.0301.00 (reference)Q10.0041.00 (reference)
Q20.8890.92 (0.29–2.85)Q20.0302.99 (1.11–8.07)
Q30.7100.81 (0.28–2.34)Q30.3081.63 (0.64–4.19)
Q40.0463.00 (1.02–8.84)Q40.0016.07 (2.16–17.07)

; ; Model 1: age and BMI were selected. Model 2: age, BMI, FPG, and LDL were selected. Model 3: age, BMI, FPG, LDL, and HOMA-IR were selected.

4. Discussion

In our study, we found that a high serum UA level was accompanied with more severe metabolic disorders such as extreme adiposity, hypertension, glucose metabolic disorder, hyperlipidemia, and AN. Moreover, serum UA levels may be an important risk factor for the occurrence of AN independent of BMI, FPG, LDL, and even HOMA-IR.

The association between serum UA and MetS components is in accordance with previous perspective studies. Zhang et al. performed a longitudinal cohort study to explore the relationship between serum UA levels and MetS in Chinese Han urban male population and found serum UA might occur as an important risk factor of MetS [19]. The same results were found in middle-aged Korean men [20] and European individuals [21]. Hyperuricemic animal model caused by fructose-rich diet could induce the components of MetS [22]. Uric acid-lowering drugs (allopurinol or benzbromarone) could blunt the occurrence of MetS, while the rats in the control group developed increased body weight, SBP, hyperinsulinemia, and hypertriglyceridemia. Also, when allopurinol was prescribed, the components of MetS could be prevented [23]. The proposed interlinked mechanisms to explain the relationship between serum UA and MetS apart from insulin resistance were oxidative stress [24], endothelial dysfunction [25], renal microvascular lesions [26], and the imbalance in vasodilation (reduction of nitric oxide [25]) and vasoconstriction (increase of renin-angiotensin-aldosterone-system [19]).

AN is associated with a high prevalence of MetS. In the past years, insulin resistance has been considered as the most important risk factor for AN. The possible explanation was that hyperinsulinemia induced by insulin resistance activated IGF receptors, which were considered to be responsible for mediating the effects of insulin on the proliferation of cells [27, 28], therefore leading to the thickening and hyperpigmentation of the skin of the epidermis and contributing to the development of AN. However, in our study, even when we adjusted the HOMA-IR, we still got a positive relationship between serum UA levels and the occurrence of AN. This indicated that the serum UA levels might be another important factor in the process of AN independent of insulin resistance. We hypothesize that AN prevalence was minimal at Q3 (OR 1.63, 95% CI 0.64–4.19 for UA 459~528 μmol/L in males and OR 0.81, 95% CI 0.28–2.34 for UA 361~424 μmol/L in females) and increased significantly both just below this range at Q2 (2.99, 1.11–8.07 for UA 394~458 μmol/L in males and 0.92, 0.29–2.85 for UA 311~360 μmol/L in females) and throughout this range at Q4 (6.07, 2.16–17.07 for UA μmol/L in males and 3.00, 1.02–8.84 for UA μmol/L in females). This relation was just like a U-shaped curve. This is similar to the relationship between BMI and all-cause mortality [29]. The underlying mechanism by which HUA leads to AN may include the following points: firstly, high serum UA induced oxidative stress and increased reactive oxygen species (ROS) levels; secondly, elevated ROS levels subsequently activated phospho-insulin receptor substrate-1 (IRS-1) (Ser307/312) and then inhibited phospho-Akt (Ser473), which finally inhibited the downstream transduction of insulin signaling and led to insulin resistance [30]. However, the relationship between the level of serum UA and AN development was still unclear; further studies were expected to examine the mechanism.

Serum UA is a commonly measured biochemical parameter in health examinations, and our results provide epidemiological evidence that serum UA might be an independent risk factor for the occurrence of AN. Therefore, serum UA level as a serum maker might be used to select obese individuals in order to predict AN and more severe metabolic disorders.

The present study has its limitations. Firstly, in the recent studies, with the fact that the relationship between serum UA levels and metabolic syndrome components was obvious in obese patients instead of normal-weight ones [31, 32], we did not recruit normal-weight patients in our study. Secondly, this study did not assess different lifestyles, dietary habits, and regions, which were found to influence the serum UA levels. Thirdly, this was a cross-sectional study; therefore, it is difficult to ascertain a causal relationship between serum UA levels and occurrence of AN. Finally, the sample size in this study was relatively small which could limit the generalization of our findings. It is also possible that unmeasured confounding variables may exist. Further studies are warranted to elucidate the detailed mechanisms by which high serum UA levels contribute to the prevalence for AN.

In conclusion, our study found that high serum UA levels were associated with more severe metabolic abnormalities including increased BMI, hypertension, hyperglycemia, and hyperlipidemia. Serum uric acid was positively associated with AN and increased the risk of AN. Serum uric acid levels may be a novel and useful method to select obese patients to prevent the occurrence of AN. However, since this study was a cross-sectional study, further evidences in vitro and in vivo are needed to investigate the direct or indirect relationship.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

This paper was supported by the National Natural Science Foundation of China (NSFC 81500650) and Fundamental Research Funds for the Central Universities (1501219107).

References

  1. K. Chizyński and M. Rózycka, “Hyperuricemia,” Polski Merkuriusz Lekarski, vol. 19, no. 113, pp. 693–696, 2005. View at: Google Scholar
  2. D. Conen, V. Wietlisbach, P. Bovet et al., “Prevalence of hyperuricemia and relation of serum uric acid with cardiovascular risk factors in a developing country,” BMC Public Health, vol. 4, article 9, 2004. View at: Publisher Site | Google Scholar
  3. Y. Zhu, B. J. Pandya, and H. K. Choi, “Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition,” Arthritis & Rheumatism, vol. 63, no. 10, pp. 3136–3141, 2011. View at: Publisher Site | Google Scholar
  4. T. S. Perlstein, O. Gumieniak, G. H. Williams et al., “Uric acid and the development of hypertension: the normative aging study,” Hypertension, vol. 48, no. 6, pp. 1031–1036, 2006. View at: Publisher Site | Google Scholar
  5. A. Dehghan, M. van Hoek, E. J. Sijbrands, A. Hofman, and J. C. Witteman, “High serum uric acid as a novel risk factor for type 2 diabetes,” Diabetes Care, vol. 31, no. 2, pp. 361–362, 2008. View at: Publisher Site | Google Scholar
  6. T. Yang, C. H. Chu, C. H. Bai et al., “Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study,” Atherosclerosis, vol. 220, no. 2, pp. 525–531, 2012. View at: Publisher Site | Google Scholar
  7. C. F. Kuo, S. F. Luo, L. C. See et al., “Hyperuricaemia and accelerated reduction in renal function,” Scandinavian Journal of Rheumatology, vol. 40, no. 2, pp. 116–121, 2011. View at: Publisher Site | Google Scholar
  8. S. D. Lin, D. H. Tsai, and S. R. Hsu, “Association between serum uric acid level and components of the metabolic syndrome,” Journal of the Chinese Medical Association, vol. 69, no. 11, pp. 512–516, 2006. View at: Publisher Site | Google Scholar
  9. M. Liu, Y. He, B. Jiang et al., “Association between serum uric acid level and metabolic syndrome and its sex difference in a Chinese community elderly population,” International Journal of Endocrinology, vol. 2014, Article ID 754678, 11 pages, 2014. View at: Publisher Site | Google Scholar
  10. S. K. Sah, S. Khatiwada, S. Pandey et al., “Association of high-sensitivity C-reactive protein and uric acid with the metabolic syndrome components,” Springerplus, vol. 5, article 269, 2016. View at: Publisher Site | Google Scholar
  11. C. A. Stuart, C. R. Gilkison, M. M. Smith, A. M. Bosma, B. S. Keenan, and M. Nagamani, “Acanthosis nigricans as a risk factor for non-insulin dependent diabetes mellitus,” Clinical Pediatrics, vol. 37, no. 2, pp. 73–79, 1998. View at: Publisher Site | Google Scholar
  12. L. H. Jones and M. Ficca, “Is Acanthosis nigricans a reliable indicator for risk of type 2 diabetes?” The Journal of School Nursing, vol. 23, no. 5, pp. 247–251, 2007. View at: Publisher Site | Google Scholar
  13. S. Sinha and R. A. Schwartz, “Juvenile Acanthosis nigricans,” Journal of the American Academy of Dermatology, vol. 57, no. 3, pp. 502–508, 2007. View at: Publisher Site | Google Scholar
  14. J. P. Burke, D. E. Hale, H. P. Hazuda, and M. P. Stern, “A quantitative scale of Acanthosis nigricans,” Diabetes Care, vol. 22, no. 10, pp. 1655–1659, 1999. View at: Publisher Site | Google Scholar
  15. C. A. Stuart, M. M. Smith, C. R. Gilkson, S. Shaheb, and R. M. Stahn, “Acanthosis nigricans among Native Americans: an indicator of high diabetes risk,” American Journal of Public Health, vol. 84, no. 11, pp. 1839–1842, 1994. View at: Publisher Site | Google Scholar
  16. C. Gilkson and C. A. Stuart, “Assessment of patients with acanthosis nigricans skin lesion for hyperinsulinaemia, insulin resistance and diabetes risk,” The Nurse Practitioner, vol. 17, no. 2, pp. 26–44, 1992. View at: Google Scholar
  17. Y. Huang, J. Yang, Y. Li et al., “FGF21 is associated with Acanthosis nigricans in obese patients,” International Journal of Endocrinology, vol. 2016, Article ID 1658062, 7 pages, 2016. View at: Publisher Site | Google Scholar
  18. P. Allard, E. E. Delvin, G. Paradis et al., “Distribution of fasting plasma insulin, free fatty acids, and glucose concentrations and of homeostasis model assessment of insulin resistance in a representative sample of Quebec children and adolescents,” Clinical Chemistry, vol. 49, no. 4, pp. 644–649, 2003. View at: Publisher Site | Google Scholar
  19. Q. Zhang, C. Zhang, X. Song et al., “A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population,” BMC Public Health, vol. 12, article 419, 2012. View at: Publisher Site | Google Scholar
  20. J. K. Lee, J. H. Ryoo, J. M. Choi, and S. K. Park, “Serum uric acid level and the incidence of metabolic syndrome in middle-aged Korean men: a 5-year follow-up study,” Journal of Preventive Medicine and Public Health, vol. 47, no. 6, pp. 317–326, 2014. View at: Publisher Site | Google Scholar
  21. N. Babio, M. A. Martínez-González, R. Estruch et al., “Associations between serum uric acid concentrations and metabolic syndrome and its components in the PREDIMED study,” Nutrition, Metabolism, and Cardiovascular Diseases, vol. 25, no. 2, pp. 173–180, 2015. View at: Publisher Site | Google Scholar
  22. P. J. Havel, “Dietary fructose: implications for dysregulation of energy homeostasis and lipid/carbohydrate metabolism,” Nutrition Reviews, vol. 63, no. 5, pp. 133–157, 2005. View at: Publisher Site | Google Scholar
  23. T. Nakagawa, H. Hu, S. Zharikov et al., “A causal role for uric acid in fructose-induced metabolic syndrome,” American Journal of Physiology—Renal Physiology, vol. 290, no. 3, pp. F625–F631, 2006. View at: Publisher Site | Google Scholar
  24. Y. Y. Sautin, T. Nakagawa, S. Zharikov, and R. J. Johnson, “Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidase-mediated oxidative/nitrosative stress,” American Journal of Physiology—Cell Physiology, vol. 293, no. 2, pp. C584–C596, 2007. View at: Publisher Site | Google Scholar
  25. G. Mercuro, C. Vitale, E. Cerquetani et al., “Effect of hyperuricemia upon endothelial function in patients at increased cardiovascular risk,” The American Journal of Cardiology, vol. 94, no. 7, pp. 932–935, 2004. View at: Publisher Site | Google Scholar
  26. D. I. Feig, D. H. Kang, and R. J. Johnson, “Uric acid and cardiovascular risk,” The New England Journal of Medicine, vol. 359, no. 17, pp. 1811–1821, 2008. View at: Publisher Site | Google Scholar
  27. J. S. Flier, P. Usher, and A. C. Moses, “Monoclonal antibody to the type I insulin-like growth factor (IGF-I) receptor blocks IGF-I receptormediated DNA synthesis: clarification of the mitogenic mechanisms of IGF-I and insulin in human skin fibroblasts,” Proceedings of the National Academy of Sciences of the United States of America, vol. 83, no. 3, pp. 664–668, 1986. View at: Publisher Site | Google Scholar
  28. G. L. King, C. R. Kahn, M. M. Rechler, and S. P. Nissley, “Direct demonstration of separate receptors for growth and metabolic activities of insulin and multiplication-stimulation activity (an insulin-like growth factor) using antibodies to the insulin receptor,” The Journal of Clinical Investigation, vol. 66, no. 1, pp. 130–140, 1980. View at: Publisher Site | Google Scholar
  29. E. D. Angelantonio, S. N. Bhupathiraju, D. Wormser et al., “Body-mass index and all-cause mortality: individual-participant-datameta-analysis of 239 prospective studies in four continents,” The Lancet, vol. 388, no. 10046, pp. 776–786, 2016. View at: Publisher Site | Google Scholar
  30. Y. Zhu, Y. Hu, T. Huang et al., “High uric acid directly inhibits insulin signalling and induces insulin resistance,” Biochemical and Biophysical Research Communications, vol. 447, no. 4, pp. 707–714, 2014. View at: Publisher Site | Google Scholar
  31. J. V. Norvik, H. M. Storhaug, K. Ytrehus et al., “Overweight modifies the longitudinal association between uric acid and some components of the metabolicsyndrome: the Tromsø study,” BMC Cardiovascular Disorders, vol. 16, no. 1, article 85, 2016. View at: Publisher Site | Google Scholar
  32. E. Oda, “Serum uric acid is an independent predictor of metabolic syndrome in a Japanese health screening population,” Heart and Vessels, vol. 29, no. 4, pp. 496–503, 2014. View at: Publisher Site | Google Scholar

Copyright © 2017 Cuiling Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views2161
Downloads891
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.