Evaluation of Association of Hyperuricaemia with Metabolic Syndrome and Insulin Resistance

Uric acid is the end product of dietary and endogenous purine metabolism in humans [1]. Serum Uric Acid (SUA) concentrations depend on the balance between the intake, endogenous synthesis, excretion ratio and metabolism of purines. Any alteration in the balance between these factors could trigger hyperuricaemia, defined as a SUA concentration >6.8 mg/dL [2]. Studies have shown that high concentrations of SUA have been associated with an early onset of hypertension and predicts rise in blood pressure, an increase in body and triglyceride levels [3-5]. In several experimental studies it has been observed that, an elevated SUA concentrations precede much before the development of insulin resistance and thus suggests that hyperuricaemia could be a new marker for MetS [6-11].

MetS is an important public health problem affecting nearly 25.9% of the world population. Earlier studies demonstrated high prevalence of MetS among patients with gout [6-8]. Some authors claimed the existence of association between hyperuricaemia and MetS even in healthy individuals [6-8]. Many cross-sectional studies have demonstrated a relationship between increased SUA concentrations and MetS prevalence [9-11].

It is proposed that UA is one of the determinants of the MetS [11]. The odds of developing MetS are 1.6 fold times higher in individuals with elevated levels of SUA [12]. But there was no established relationship between hyperuricaemia, insulin resistance and components of MetS. The present study was aimed to evaluate the association of hyperuricaemia with MetS and insulin resistance.

Materials and Methods

In this case control study 60 subjects (30 cases and 30 controls) with MetS aged 19 years and above were included by following convenient sample technique. The sample size was calculated statistically, with power of the study at 80%, 95% Confidence interval, ratio of controls to cases as 1, anticipated odds ratio of 5 and proportion of controls with exposure as 25%. It was calculated from Open Epi, Version 3.0. The MetS was defined according to the diagnostic criteria of ATPIII guidelines [13]. The study was carried out for a period of 2 years from August 2013 to July 2015 in a tertiary care hospital. All the study participants were recruited after obtaining a written informed consent. Prior to the initiation the study protocol was approved by institutional ethics committee. Patients with type-1 diabetes mellitus, chronic kidney disease, lymphoproliferative disorders, haemolytic disorders, myeloproliferative disorders, using drugs like salicylates, diuretics like thiazides, levodopa, ethambutol, cyclosporine, nicotinic acid, pyrazinamide and who consume alcohol≥10 grams per day were excluded from the study. Hyperuricaemia is defined as SUA levels >6.8mg/dL in both males and females [2]. Based on SUA levels the total 60 subjects were divided into two groups namely, controls (n = 30) and cases (n = 30). MetS subjects with serum uric acid < 6.8mg/dL served as controls and > 6.8mg/dL considered as cases. The blood pressure and anthropometric measurements including weight, height, and waist circumferences were noted and accordingly Body Mass Index (BMI) was calculated. Homeostasis model assessment (HOMA-IR) 2.0 computerized method was used to measure insulin resistance [14]. All the other biochemical parameters and SUA levels were measured by using auto analyser.

Statistical Analysis

Data was presented as Mean±SD. The categorical variables were presented as proportions. Independent sample t-test was done to find out difference between group-I and group-II. Chi square test was done for categorical variables. Multiple logistic regression was carried out to find out independent predictors of hyperuricaemia. Receiver Operating Curve (ROC) was plotted to find out the optimal cut-off value for insulin resistance. A p-value <0.05 was considered statistically significant. Data was analysed using statistical package of social sciences (SPSS version 17.0).

Results

The mean age and male: female ratio of the study population was found to be 52.42±10.60 years and 35:25, respectively. Based on SUA levels, 12 (40%) females and 18 (60%) males were found to have hyperuricaemia. A significant difference in mean HOMA-IR (p=0.009), systolic blood pressure (< 0.001), and triglycerides levels (p=0.027) were observed between these two groups [Table/Fig-1]. However, after logistic regression there was no significant association between hyperuricaemia and components of MetS. The ROC curve for insulin resistance was found to be significant with area under the curve 0.702 (p=0.007, 95% CI= 0.56 -0.83) [Table/Fig-2]. Based on ROC curve an optimal cutoff point for HOMA IR was found to be 3.4 with 80% (95% CI=0.59 -0.90) sensitivity and 68% (95% CI= 0.53-0.82) specificity, respectively. Based on HOMA IR cut-off value, 37 subjects had HOMA IR>3.4. Among them 13 (35.1%) were found to have normal uric acid levels and remaining 24 (64.9%) were found to have hyperuricaemia.

[Table/Fig-1]:

Clinical characteristics of study population.

Variables	Controls (n =30)Normal uricacid	Cases (n=30)Hyperuricaemia	p-value
Serum Uric acid (mg/dL)	4.63±1.25	8.22±1.37	< 0.001
Systolic blood pressure (mmHg)	138.40±14.63	153.20±9.30	<0.001
HOMA-IR	4.59±3.60	7.79±5.35	0.009
Triglycerides (mg/dL)	162.90±62.07	198.16±58.33	0.027
Weight (kg)	65.36±13.6	70.80±13.36	0.124
Height (cm)	160.63±8.57	161.13±8.43	0.824
Body mass index (kg/m2)	25.67±4.24	27.28±4.61	0.162
Waist circumference (cm)	90.66±10.54	95.20±6.60	0.05
Fasting blood sugar (mg/dL)	142.43±54.09	165.36±50.94	0.09
Fasting Insulin (μIU/ml)	13.11±9.7	18.63±11.86	0.054
HDL (mg/dL)	39.12±7.42	38.80±6.97	0.079
Males	17	18	0.793
Females	13	12	0.793

* HOMA-IR- Homeostasis model assessment.

HDL-C – High density lipoprotein cholesterol.

[Table/Fig-2]:

Receiver operating curve showing optimal cut-off for insulin resistance.

After adjusting for waist circumference, fasting blood sugar and triglycerides, HOMA IR > 3.4 was independently associated with hyperuricaemia (OR=5.79, 95% CI=1.6- 20.69, p = 0.007) [Table/Fig-3]. The other components of MetS were not significantly associated with hyperuricaemia [Table/Fig-3].

[Table/Fig-3]:

Multiple logistic regression showing independent predictors of hyperuricaemia.

Variables	B (95% CI)	Adjusted Odds Ratio	p-value
HOMA-IR	1.75(1.62-20.69)	5.79	0.007
Fasting blood sugar	0.46(0.20-12.55)	1.59	0.656
Triglycerides	1.03(0.72-11.02)	2.82	0135

* HOMA-IR- Homeostasis model assessment

Discussion

The present study was aimed to find out the association of hyperuricaemia with MetS and insulin resistance. This study demonstrated that hyperuricaemia is directly proportional to insulin resistance as an independent factor. This association is relevant once the SUA is more than 6.8mg/dL.

Hyperuricaemia is associated with wide range of diseases including MetS [15,16]. Both cross-sectional and prospective studies have consistently shown that uric acid levels were associated with the risk of MetS [17-20]. However, whether the uric acid has the causal effects on MetS still remains to be investigated [21]. Chen LY et al., observed negative correlation between HDL-C and UA [12]. The likely mechanism is the relationship between decreased HDL-C and insulin resistance [22]. In our study there was no correlation between HDL, triglycerides and uric acid. Over all in our study the components of MetS was not associated with hyperuricaemia which is in discordance with previous studies [17-21]. This may be due to their individual life style patterns.

However, in our study it was also observed that insulin resistance was independently associated with hyperuricaemia even after adjusting for other components of MetS. It has been suggested that hyperuricaemia appears to be a part of insulin resistance syndrome due to the resemblance between insulin resistance syndrome and hyperuricaemia [23-25]. A possible explanation has been supported by several epidemiological studies [23-25]. In a study done with Jewish population it has been observed that a significant positive linear correlation was observed between FSA acid levels and plasma insulin responses (sum of 1- and 2-h post oral glucose levels) in both males and females [25]. Subsequent study showed a positive relationship after adjusting for age, gender, overall obesity and abdominal obesity [26]. In a case-control study, Sinagra D et al., demonstrated that insulin resistance to be independently associated with fasting SUA [27]. Experimental studies also indicated that uric acid might play a role in insulin resistance promoting secretion of inflammatory factors and adipocytokine [21,22]. This is in concordance with our study. It has been suggested that the positive association between the insulin resistance syndrome and fasting hyperuricaemia is partly explained by the fact that hyperinsulinemia may decrease the renal excretion of uric acid additionally; insulin could indirectly act on UA, since there is an association between hyperinsulinemia and hypertriglyceridemia [28]. However, in these epidemiological studies optimal cut-off values for insulin resistance was not defined. In the present study, we found that optimal cut-off for insulin resistance was 3.4 and HOMA IR >3.4 were significantly associated with hyperuricaemia.

Limitation

Firstly, this study can not be generalised to whole population because insulin resistance varies across different ethnic groups. Secondly it is a cross sectional study and hence, further prospective studies are required with larger sample size to establish the causal relationship between hyperuricaemia and insulin resistance.

Conclusion

Metabolic syndrome is a common disorder in the community associated with insulin resistance. In our study insulin resistance is independently associated with hyperuricaemia in subjects with MetS. Hence, high uric acid levels can be considered as an indirect marker of insulin resistance which is affordable and rapid tool.

* HOMA-IR- Homeostasis model assessment.HDL-C – High density lipoprotein cholesterol.* HOMA-IR- Homeostasis model assessment

[1]. Richette P, Bardin T, Gout Lancet 2010 375:318  [Google Scholar]

[2]. Bringhurst RF, Demay BM, Krane MS, Kronenberg MH, Bone and mineral metabolism in health and disease In: Harrisons, editor. principle of internal medicine 2012 18th ed(2)New YorkMcGraw Hill:3182  [Google Scholar]

[3]. Feig DI, Madero M, Jalal DI, Sanchez-Lozada LG, Johnson RJ, Uric acid and the origins of hypertension J Pediatr 2013 162:896  [Google Scholar]

[4]. Conen D, Wietlisbach V, Bovet P, Shamlaye C, Risen W, Paccaud F, Prevalence of hyperuricaemia and relation of serum uric acid with cardiovascular risk factors in a developing country BMC Public Health 2004 4:9  [Google Scholar]

[5]. Keenan T, Blaha MJ, Nasir K, Silverman MG, Tota-Maharaj R, Carvalho JAM, Relation of uric acid to serum levels of highsensitivity C-reactive protein, triglycerides, and high-density lipoprotein cholesterol and to hepatic steatosis Am J Cardiol 2012 110:1787-92.  [Google Scholar]

[6]. Johnson RJ, Nakagawa T, Sanchez-Lozada LG, Shafiu M, Sundaram S, Le M, Sugar, uric acid, and the etiology of diabetes and obesity Diabetes 2013 62:3307  [Google Scholar]

[7]. Choi Y-J, Yoon Y, Lee K-Y, Hien TT, Kang KW, Kim K-C, Uric acid induces endothelial dysfunction by vascular insulin resistance associated with the impairment of nitric oxide synthesis FASEB J 2014 28:3197  [Google Scholar]

[8]. Zhu Y, Hu Y, Huang T, Zhang Y, Li Z, Luo C, High uric acid directly inhibits insulin signalling and induces insulin resistance Biochem Biophys Res Commun 2014 447:707  [Google Scholar]

[9]. Billiet L, Doaty S, Katz JD, Velasquez MT, Review of hyperuricaemia as new marker for metabolic syndrome ISRN Rheumatol 2014 2014:852954  [Google Scholar]

[10]. You L, Liu A, Wuyun G, Wu H, Wang P, Prevalence of hyperuricaemia and the relationship between serum uric acid and metabolic syndrome in the Asian Mongolian area J Arth Trombos 2014 21:355  [Google Scholar]

[11]. Onat A, Uyarel H, Hergenc G, Karabulut A, Albayrak S, Sari I, Serum uricacid is a determinant of metabolic syndrome in a population-based study Am J Hypertens 2006 19:1055-62.  [Google Scholar]

[12]. Chen LY, Zhu WH, Chen ZW, Dai HL, Ren JJ, Chen JH, Relationship between hyperuricaemia and metabolic syndrome J Zhejiang Univ Sci B 2007 8:593-98.  [Google Scholar]

[13]. Galper BZ, Wang YC, Einstein AJ, Strategies for primary prevention of coronary heart disease based on risk stratification by the ACC/AHA lipid guidelines, ATP III guidelines, coronary calcium scoring, and c-reactive protein and a Global Treat-All Strategy: A Comparative-Effectiveness Modelling Study PLoS One 2015 10:1-37.  [Google Scholar]

[14]. Srinivasan MP, Kamath PK, Bhat NM, Pai ND, Manjrekar PA, Mahabala C, Factors associated with no apparent coronary artery disease in patients with type 2 diabetes mellitus for more than 10 years of duration: A case control study Cardiovasc Diabetol 2015 14:146  [Google Scholar]

[15]. Ford ES, Li C, Cook S, Choi HK, Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents Circulation 2007 :2526-32.  [Google Scholar]

[16]. Nejatinamini S, Ataie-Jafari A, Qorbani M, Nikoohemat S, Kelishadi R, Asayesh H, Association between serum uric acid level and metabolic syndrome components J Diabetes Metab Disord 2015 14:70  [Google Scholar]

[17]. Tuttle KR, Short RA, Johnson RJ, Sex differences in uric acid and risk factors for coronary artery disease Am J Cardiol 2001 87:1411-14.  [Google Scholar]

[18]. Chiou WK, Wang MH, Huang DH, Chiu HT, Lee YJ, Lin JD, The relationship between serum uric acid level and metabolic syndrome: differences by sex and age in Taiwanese J Epidemiol 2010 20:219-24.  [Google Scholar]

[19]. Nan H, Qiao Q, Söderberg S, Gao W, Zimmet P, Shaw J, Serum uric acid and components of the metabolic syndrome in non-diabetic populations in Mauritian Indians and Creoles and in Chinese in Qingdao, China Metab Syndr Relat Disord 2008 6:47-57.  [Google Scholar]

[20]. Yang T, Chu CH, Bai CH, You SL, Chou YC, Chou W, Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study Atherosclerosis 2012 220:525-31.  [Google Scholar]

[21]. Zhang Q, Zhang C, Song X, Lin H, Zhang D, Meng W, A longitudinal cohort based association study between uric acid level and metabolic syndrome in Han Chinese urban population BMC Public Health 2012 12:419  [Google Scholar]

[22]. Schmidt MI, Watson RL, Duncan BB, Metcalf P, Brancati FL, Sharrett AR, Clustering of dyslipidemia, hyperuricaemia, diabetes, and hypertension and its association with fasting insulin and central and overall obesity in a general population. Atherosclerosis Risk in Communities Study Investigators Metabolism 1996 45:699-706.  [Google Scholar]

[23]. Vuorinen-Markkola H, Yki-Järvinen H, Hyperuricaemia and insulin resistance Journal of Clinical Endocrinology and Metabolism 1994 78:25-28.  [Google Scholar]

[24]. Wei CY, Sun CC, Wei JC, Tai HC, Sun CA, Chung CF, Association between hyperuricaemia and metabolic syndrome: an epidemiological study of a labor force population in taiwan Biomed Res Int 2015 2015:369179  [Google Scholar]

[25]. Cigolini M, Targher G, Tonoli M, Manara F, Muggeo M, De Sandre G, Hyperuricaemia: relationships to body fat distribution and other components of the insulin resistance syndrome in 38-yearold healthy men and women Int J Obes Relat Metab Disord 1995 19:92-96.  [Google Scholar]

[26]. Modan M, Halkin K, Karasik A, Lusky A, Elevated serum uric acid – a facet of hyperinsulinaemia Diabetologia 1987 30:713-18.  [Google Scholar]

[27]. Sinagra D, Greci D, Scarpitta AM, Bonaventura V, Serum uric acid, insulin secretion and resistance in nonhyperuricaemic and hyperuricaemic obese female subjects Int J Obes Relat Metab Disord 1996 20:1041-43.  [Google Scholar]

[28]. Galvan AQ, Natali A, Baldi S, Frascerra S, Sanna G, Ciociaro D, Effect of insulin on uric acid excretion in humans Am J Physiol 1995 31:E1-E5.  [Google Scholar]