http://informahealthcare.com/arp ISSN: 1381-3455 (print), 1744-4160 (electronic) Arch Physiol Biochem, 2014; 120(3): 119–122 ! 2014 Informa UK Ltd. DOI: 10.3109/13813455.2014.924145
ORIGINAL ARTICLE
Serum uric acid levels and metabolic syndrome
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Sara Ciarla, Manuela Struglia, Paolo Giorgini, Rinaldo Striuli, Stefano Necozione, Giuliana Properzi, and Claudio Ferri University of L’Aquila, Department of Life, Health and Environmental Sciences, San Salvatore Hospital, L’Aquila, Italy
Abstract
Keywords
Objective: To investigate the relationship among serum uric acid levels and metabolic syndrome. Materials and methods: Anthropometric parameters, serum uric acid and metabolic parameters were evaluated in 139 subjects. Results: Serum uric acid levels were significantly higher in subjects with than without metabolic syndrome (p50.0001), and raised gradually with the increasing number of metabolic syndrome components (p for trend50.0001). Serum uric acid significantly correlated with various anthropometric and serum metabolic parameters. Discussion and conclusions: Serum uric acid levels were higher in individuals with rather than without metabolic syndrome and raised gradually as the number of metabolic syndrome components increased. The relationship between serum uric acid levels and various metabolic parameters suggests that uric acid might be considered as a component of metabolic syndrome. Context: Hyperuricemia is a common finding in patients with the metabolic syndrome. Recent studies indicated that hyperuricemia may be also a predictor of metabolic syndrome development.
Cardiovascular risk, insulin resistance, metabolic syndrome, Uric acid
Introduction The metabolic syndrome (MS) is commonly defined as the cluster of several abnormalities typically including central adiposity, abnormal blood pressure, dyslipidemia, and dysglycemia (NCEP, 2001). In particular, the diagnosis of MS is made when three or more of the risk determinants are present, in agreement to the National Cholesterol Education Program (NCEP)/Adult Treatment Panel III (ATP-III) criteria (NCEP, 2001) (Table 1). MS is often associated with the subsequent development of type 2 diabetes mellitus and cardiovascular disease (Ford et al., 2007). In addition to the well known NCEP/ATP-III definition, other conditions have been suggested to be associated with MS, including liver steatosis, increased oxidative stress, endothelial dysfunction, low grade systemic inflammation and hyperuricemia (Ford et al., 2007). With regard to this latter, elevated serum uric acid (SUA) levels are commonly seen in patients with MS, although its clinical meaning is still controversial and often underestimated (Borges et al., 2012). Traditionally, high levels of SUA in MS are, at least in part, attributed to insulin resistance. Concordantly, renal clearance of urate is inversely related to the degree of insulin resistance, while higher concentrations of insulin are known to reduce
Correspondence: Sara Ciarla, University of L’Aquila, Department of Life, Health and Environmental Sciences, L’Aquila, Italy. San Salvatore Hospital, Edificio Delta 6, Viale San Salvatore, 67100, Coppito, L’Aquila, Italy. Tel/Fax: +39 +862 368621. E-mail: [email protected]
History Received 19 March 2014 Revised 7 May 2014 Accepted 11 May 2014 Published online 10 June 2014
the renal excretion of urate (Facchini et al., 1991; Muscelli et al., 1996). Recent studies also suggest that elevated SUA levels can stimulate oxidative stress, endothelial dysfunction and vasoconstriction and, thereby, induce low grade systemic inflammation and favour the development of MS and cardiovascular disease (Baldwin et al., 2011). Concordant to all of these observations, a growing bulk of evidence indicates that elevated SUA levels might precede the development of MS (Fang and Alderman, 2000; Grassi et al., 2013; Ishizaka et al., 2005) and the 2012 European Society of Cardiology Guidelines on cardiovascular prevention indicate the measurement of serum uric acid levels as a component of the routine tests, i.e. to be assessed in all patients in order to stratify their cardiovascular risk (Perk et al., 2012). Despite these findings, a recent meta-analysis did not confirm a relationship between decrements in SUA levels and cardiovascular event rate (Savarese et al., 2013). Similarly, recent epidemiological data failed to demonstrate any correlation between SUA levels and cardiovascular mortality (Fang and Alderman, 2000). The reasons leading to these discrepancies are likely to be related with the study populations, including patients in primary and secondary prevention, often already on treatment with either allopurinol or febuxostat. The aim of the present study was to investigate the relationship among SUA levels, MS and insulin resistance in consecutive untreated patients with no confounding factors, i.e. previous diagnosis of hyperuricemia and/or gout; and who were in primary prevention, i.e. with no personal history of cardiovascular accidents.
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Arch Physiol Biochem, 2014; 120(3): 119–122
Table 1. Clinical identification of the metabolic syndrome according to National Cholesterol Education Program Adult Treatment Panel III criteria. Risk factor Waist circumference (cm) Blood pressure (mmHg) Fasting glucose (mg/dl) Triglycerides (mg/dl) HDL-cholesterol (mg/dl)
Defining level
102 cm in men, 88 cm in women 130/85 mmHg 100 mg/dl 150 mg/dl 40 mg/dl in men, 50 mg/dl in women
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Patients and methods Patients A consecutive cohort of outpatients was observed at our Division of Internal Medicine – San Salvatore Hospital, L’Aquila, Italy – between September 2012 and December 2012. A total of 139 patients (67 males and 72 females) aged 18 to 70 years (mean age ± SD ¼ 47 ± 12 years) who underwent health examination was enrolled. Exclusion criteria consisted of an age 518 years or 470 years, secondary hypertension, calculated glomerular filtration rate 5 60 ml/min/1.73 m2, type 1 or 2 diabetes mellitus, use of any drug known to interfere with the uric acid metabolism (i.e. diuretics, ASA, losartan, postmenopausal hormone therapy), familiar or secondary dyslipidemia, known cardiovascular and/or cerebrovascular diseases, cancer, pregnancy and nursing, alcoholism, abuse of drugs and psychic alterations.
Methods Demographic and clinical data were collected with standardized interview sheets. Body weight (kg) and standing height (m) were measured using a scale and a stadiometer after the subjects were fasting overnight and wearing only light underwear. Body mass index (BMI) was then calculated as weight in kilograms divided by the square of height in metres (WHO, 1995). Waist circumference (cm) was measured using a steel measuring tape at the level of the iliac crest at the end of a normal expiration (USDHHS, 1996). Blood pressure measurements (systolic blood pressure, SBP; diastolic blood pressure, DBP) were performed twice in all subjects, in the sitting position, using a mercury sphygmomanometer and the auscultatory technique, according to European Society of Hypertension Recommendations (Mancia et al., 2013). After the above procedures, blood samples were collected in the morning after 8–12 hours at fast. Serum low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglyceride, high-sensitive C-reactive protein (hs-CRP), creatinine, SUA, fasting glucose and insulin levels were determined by standard laboratory methods. We used the Modification of Diet in Renal Disease (MDRD) formula to estimate glomerular filtration rate (eGFR) expressed in ml/ min/1.73 m2. Insulin resistance was estimated by the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) equation, calculated as [(fasting plasma glucose, mmol/l) (fasting serum insulin, mU/ml)]/22,5 (Matthews et al., 1985).
Statistical analysis All statistical analysis were performed by the Statistical Analysis System (SAS) software (SAS Institute Inc., Cary, NC, USA). Unless otherwise stated, all results are expressed as mean ± standard deviation. The distribution of values for each variable was analysed by the Shapiro-Wilk normality test. In order to non-normal distribution of variables, analysis of variance was performed with non-parametric ANOVA (Kruskal-Wallis test). The posthoc analysis was made according to Conover. The Spearman coefficient was used for linear correlations between variables. Multiple linear regression after logarithmic transformation of variables was used to evaluate the relative weight and persistence of the relationship among variables. A p value 50.05 was considered as statistically significant for all analysis.
Results Sixty-two patients (49 ± 10 years) resulted affected by MS (NCEP/ATP-III criteria (NCEP, 2001)), while 77 patients (42 ± 12 years) had no MS. The whole study population was divided into six groups according to the presence of 0 (healthy controls, n ¼ 25), 1 (n ¼ 29), 2 (n ¼ 23), 3 (n ¼ 27), 4 (n ¼ 24) or 5 (n ¼ 11) MS components. Patient’s data are showed in Table 2. SUA levels ranged from 2.1 mg/dl to 11.5 mg/dl (mean ± SD ¼ 5.46 ± 1.80 mg/dl), being significantly higher between males (6.17 ± 1.63 mg/dl) than females (4.80 ± 1.71 mg/dl) (p50.0001). Mean SUA concentrations were also significantly higher in patients with (6.19 ± 1.84 mg/dl) than without MS (4.88 ± 1.55 mg/dl) (p50.0001). Mean SUA levels increased gradually with the number of MS components from 0 (4.10 ± 1.23 mg/dl), 1 (5.06 ± 1.54 mg/dl), 2 (5.48 ± 1.57 mg/dl), 3 (5.67 ± 1.31 mg/dl), 4 (6.34 ± 2.26 mg/dl) or 5 (7.09 ± 1.63 mg/dl) (p for trend 50.0001). In the whole study population, SUA directly correlated with serum creatinine levels (p50.0001, r ¼ 0.44), body weight (p50.0001, r ¼ 0.46), BMI (p ¼ 0.0002, r ¼ 0.31), waist circumference (p50.0001, r ¼ 0.40), fasting serum glucose (p ¼ 0.003, r ¼ 0.25) and insulin levels (p50.0001, r ¼ 0.42), HOMA-IR (p50.0001, r ¼ 0.42), serum triglyceride (p50.0001, r ¼ 0.45) and hs-CRP levels (p50.0001, r ¼ 0.37); and inversely correlated with serum HDL-cholesterol concentrations (p50.0001, r ¼ 0.51). The multiple regression analysis in the entire population SUA levels directly correlated with HOMA-IR (p ¼ 0.01), serum triglyceride (p ¼ 0.04), creatinine (p ¼ 0.02) and hs-CRP levels (p ¼ 0.03) and inversely correlated with serum HDL-cholesterol concentrations (p ¼ 0.005). HOMAIR, serum triglyceride, creatinine, hs-CRP and HDL-cholesterol levels were the main determinants of SUA levels, explaining about 40.4% of its variance (r2 ¼ 0.404, p50.001).
Discussion The current study confirms that SUA levels are higher in subjects with than without MS. In particular, SUA levels increased gradually in the study population, according to the
Serum uric acid levels and metabolic syndrome
DOI: 10.3109/13813455.2014.924145
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Table 2. Clinical and laboratory characteristics in the study population (data are expressed as mean ± standard deviation, median, range).
Age (years) Weight (kg)
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BMI (kg/m2) Waist circumference (cm) SBP (mmHg) DBP (mmHg) Creatinine (mg/dl) eGFR (ml/min/1.73m2) Uric acid (mg/dl) Blood-glucose (mg/dl) Insulinemia (mU/ml) HOMA-IR Triglycerides mg/dl) LDL-cholesterol (mg/dl) HDL-cholesterol (mg/dl) hs-CRP (mg/dl)
Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range
0 MS Component (n ¼ 25, 10 Men)
1 MS Component (n ¼ 29, 17 Men)
2 MS Components (n ¼ 23, 12 Men)
3 MS Components (n ¼ 27, 11 Men)
4 MS Components (n ¼ 24, 12 Men)
5 MS Components (n ¼ 11, 5 Men)
42.84 ± 12.48 39 21.00–73.00 64.52 ± 12.18 65 42.00–87.00 23.02 ± 2.39 23.44 18.00–26.90 82.44 ± 9.13 84 60.00–98.00 117.80 ± 9.25 120 100.00–130.00 74.20 ± 6.56 75 60.00–85.00 0.77 ± 0.14 0.72 0.56–1.10 105.12 ± 20.87 109 57.00–141.00 4.10 ± 1.24 3.9 2.10–6.10 81.72 ± 9.27 82 63.00–98.00 7.10 ± 2.81 6.93 2.68–13.12 1.44 ± 0.62 1.5 0.40–2.51 75.72 ± 25.35 76 39.00–152.00 91.60 ± 31.78 98 20.00–140.00 57.20 ± 12.97 56 41.00–92.00 0.10 ± 0.12 0.04 0.01–0.50
47.41 ± 13.63 51 25.00–70.00 78.10 ± 14.33 78 56.00–110.00 27.26 ± 5.04 26 20.80–46.50 93.24 ± 11.93 92 70.00–121.00 130.66 ± 14.51 130 105.00–155.00 80.14 ± 9.89 80 60.00–99.00 0.78 ± 0.13 0.78 0.58–1.12 102.93 ± 21.57 108 64.00–154.00 5.07 ± 1.54 5 2.90–9.50 90.07 ± 18.58 89 70.00–173.00 9.92 ± 5.28 8.6 1.70–29.16 2.22 ± 1.32 1.8 0.30–7.05 103.55 ± 30.43 96 53.00–160.00 125.83 ± 29.08 117 64.00–195.00 55.79 ± 17.40 51 26.00–103.00 0.28 ± 0.36 0.2 0.01–1.63
47.08 ± 14.84 48 19.00–70.00 80.00 ± 17.67 78 42.00–136.00 28.76 ± 6.49 27 18.20–49.50 99.74 ± 16.79 98 70.00–150.00 133.00 ± 13.37 133 110.00–160.00 83.26 ± 11.07 85 66.00–105.00 0.79 ± 0.13 0.74 0.60–1.02 99.65 ± 21.11 96 65.00–138.00 5.49 ± 1.57 5.2 3.40–9.00 87.35 ± 13.74 86 57.00–126.00 11.26 ± 4.84 11.89 3.64–22.62 2.49 ± 1.20 2.6 0.51–4.74 137.17 ± 42.58 128 70.00–222.00 129.30 ± 34.89 131 76.00–189.00 49.96 ± 15.93 48 28.00–110.00 0.46 ± 0.71 0.25 0.02–3.50
49.51 ± 11.90 50 27.00–70.00 85.15 ± 16.39 80 54.00–119.00 29.39 ± 4.86 29.6 21.00–43.50 103.19 ± 12.37 102 84.00–131.00 140.74 ± 19.10 140 100.00–180.00 85.59 ± 12.41 85 65.00–110.00 0.79 ± 0.14 0.75 0.55–1.13 99.69 ± 22.66 92.5 69.00–155.00 5.67 ± 1.31 5.5 3.50–8.60 91.89 ± 18.17 88 57.00–136.00 15.54 ± 11.86 12 4.00–64.73 3.66 ± 3.36 3.08 0.80–18.38 158.44 ± 107.26 151 60.00–571.00 132.63 ± 42.72 130 44.00–216.00 41.78 ± 11.17 42 23.00–70.00 0.58 ± 0.55 0.38 0.02–2.00
48.5 ± 10.53 49.5 28.00–70.00 94.04 ± 13.50 95.5 71.00–133.00 33.34 ± 4.75 32.5 27.40–47.00 108.42 ± 9.42 107 91.00–133.00 142.63 ± 13.99 145 109.00–167.00 89.13 ± 10.52 90 68.00–110.00 1.04 ± 1.27 0.8 0.55–7.00 108.29 ± 25.43 106 61.00–153.00 6.35 ± 2.26 5.7 2.40–11.50 107.08 ± 35.61 98.5 68.00–205.00 17.79 ± 13.73 14.85 5.90–77.33 5.27 ± 6.20 3.51 0.99–32.06 195.88 ± 95.08 188 69.00–419.00 130.50 ± 34.33 128 65.00–191.00 35.92 ± 10.12 37 10.00–53.00 0.67 ± 0.65 0.4 0.07–2.50
51.81 ± 9.72 53 31.00–62.00 102.00 ± 15.76 99 86.00–133.00 36.40 ± 5.83 34.5 30.10–47.00 115.82 ± 14.38 111 96.00–147.00 136.55 ± 9.43 140 118.00–150.00 89.91 ± 8.28 90 78.00–100.00 0.82 ± 0.16 0.79 0.60–1.10 104.82 ± 18.61 103 78.00–136.00 7.09 ± 1.64 6.8 5.20–10.60 123.45 ± 20.15 116 104.00–168.00 31.80 ± 18.38 26.4 10.76–77.33 10.33 ± 8.10 7.43 2.58–32.06 306.55 ± 182.84 255 194.00–826.00 121.73 ± 26.74 121 80.00–165.00 34.55 ± 7.34 38 20.00–45.00 0.66 ± 0.68 0.37 0.05–2.50
MS, metabolic syndrome; DS, standard deviation; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, glomerular filtration rate (estimated eGFR using Modification of Diet in Renal Disease formula); HOMA-IR, homeostasis model assessment of insulin resistance; LDL-cholesterol, low-density lipoprotein cholesterol; HDL-cholesterol, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein
number of MS components. Further SUA strongly correlated with HOMA-IR, triglyceride and HDL-cholesterol concentrations. Similar findings have been demonstrated in other studies (Ishizaka et al., 2005; Shi-Dou et al., 2006). There are several potential mechanisms that could account for the elevated concentration of SUA in MS. The renal clearance of urate is inversely related to the degree of insulin resistance (Fachinni et al., 1991; Muscelli et al., 1996). Concordantly, in our patients SUA directly correlated with
both fasting insulin levels and HOMA-IR. On the other hand, hyperuricemia could concur to the pro-inflammatory endocrine imbalance, particularly at the adipose tissue level, and thereby favours the onset of insulin resistance (Baldwin et al., 2011). According to this, in our MS patients SUA directly correlated with both BMI and hs-CRP levels. Thus, an elevated SUA concentration might represent either a consequence or a concomitant cause of insulin resistance. In our study, SUA levels were also higher in males than females. The difference in SUA levels between genders has
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been often described and is likely due to the increased renal urate clearance induced by estrogen in woman, particularly before menopause (Mumford et al., 2013). Our study has some limitations. First, it is known that hypoxia in obstructive sleep apnea syndrome (OSAS), at least in obese patients, is a potential cause of increased SUA levels (Saito et al., 2002). Unfortunately, we have no data about OSAS and are then unable to speculate on this matter. Secondly, the sample size was relatively small. Finally, the lack of longitudinal data does not allow one to estimate the relationship between SUA levels and cardiovascular events. Thus, we are also unable to assess the potential contribution of SUA to the development of cardiovascular disease. Despite these limitations, the current study strongly suggests that uric acid has a marked relation with HOMAIR and fasting insulin levels. Therefore, the same study is extremely relevant to emphasize the role of the prevention of insulin resistance and related conditions, i.e. obesity. In conclusion, hyperuricemia was strongly associated with MS. In our study cohort, the relationship between SUA levels and various metabolic parameters strongly suggests that hyperuricemia might be considered as a component of MS. Based on our data, whether SUA is an innocent bystander, a partner or a contributor to MS remains unanswered.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
References Baldwin W, McRae S, Marek G, et al. (2011). Hyperuricemia as a mediator of the proinflammatory endocrine imbalance in the adipose tissue in a murine model of the metabolic syndrome. Diabetes, 60, 1258–69. Borges RL, Ribeiro AB, Zanella MT, et al. (2012). Uric acid as a factor in the metabolic syndrome. Curr Hypertens Rep Apr, 12, 113–19. Facchini F, Chen YD, Hollenbeck CB, et al. (1991). Relationship between resistance to insulin-mediated glucose uptake, urinary uric acid clearance, and plasma uric acid concentration. JAMA, 266, 3008–11. Fang J, Alderman MH. (2000). Serum uric acid and cardiovascular mortality. The NHANES I epidemiologic follow-up study, 1971–1992. JAMA 283, 2404–10.
Arch Physiol Biochem, 2014; 120(3): 119–122
Ford ES, Li C, Cook S, et al. (2007). Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents. Circulation, 115, 2526–32. Grassi D, Ferri L, Desideri G, et al. (2013). Chronic hyperuricemia, uric acid deposit and cardiovascular risk. Curr Pharm Des, 19, 2432–8. Ishizaka N, Ishizaka Y, Toda E, et al. (2005). Association between serum uric acid, metabolic syndrome, and carotid atherosclerosis in Japanese individuals. Arterioscler Thromb Vasc Biol, 25, 1038–44. Mancia G, Fagard R, Narkiewicz K, et al. (2013). Practice guidelines for the management of arterial hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC). J Hypertens, 31, 1925–38. Matthews DR, Hosker JP, Rudenski AS, et al. (1985). Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia, 28, 412–9. Mumford SL, Dasharathy SS, Pollack AZ, et al. (2013). Serum uric acid in relation to endogenous reproductive hormones during the menstrual cycle: Findings from the BioCycle study. Hum Reprod, 28, 1853–62. Muscelli E, Natali A, Bianchi S, et al. (1996). Effect of insulin on renal sodium and uric acid handling in essential hypertension. Am J Hypertens, 9, 746–52. NCEP. (2001). Executive Summary of the Third Report of the National Cholesterol Education Program. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA, 285, 2486–97. Perk J, De Backer G, Gohlke H, et al. (2012). European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Eur Heart J, 33, 1635–701. Saito H, Nishimura M, Shibuya E, et al. (2002). Tissue hypoxia in sleep apnea syndrome assessed by uric acid and adenosine. Chest, 122, 1686–94. Savarese G, Ferri C, Trimarco B, et al. (2013). Changes in serum uric acid levels and cardiovascular events: A meta-analysis. Nutr Metab Cardiovasc Dis, 23, 707–14. Shi-Dou L, Dong-Hwa T, Shang-Ren H. (2006). Association between serum uric acid level and components of the metabolic syndrome. J Chin Med Assoc, 69, 512–16. USDHHS. (1996). US Department of Health and Human Services, National Center for Health Statistics. NHANES III Reference Manuals and Reports. Hyattsville, MD: Centers for Disease Control and Prevention. WHO. (1995). Physical status: The use and interpretation of anthropometry: Report of a WHO expert committee. World Health Organ Tech Rep Ser, 854, 1–452.
ORIGINAL ARTICLE
Serum uric acid levels and metabolic syndrome
Archives of Physiology and Biochemistry Downloaded from informahealthcare.com by University of Waterloo on 12/05/14 For personal use only.
Sara Ciarla, Manuela Struglia, Paolo Giorgini, Rinaldo Striuli, Stefano Necozione, Giuliana Properzi, and Claudio Ferri University of L’Aquila, Department of Life, Health and Environmental Sciences, San Salvatore Hospital, L’Aquila, Italy
Abstract
Keywords
Objective: To investigate the relationship among serum uric acid levels and metabolic syndrome. Materials and methods: Anthropometric parameters, serum uric acid and metabolic parameters were evaluated in 139 subjects. Results: Serum uric acid levels were significantly higher in subjects with than without metabolic syndrome (p50.0001), and raised gradually with the increasing number of metabolic syndrome components (p for trend50.0001). Serum uric acid significantly correlated with various anthropometric and serum metabolic parameters. Discussion and conclusions: Serum uric acid levels were higher in individuals with rather than without metabolic syndrome and raised gradually as the number of metabolic syndrome components increased. The relationship between serum uric acid levels and various metabolic parameters suggests that uric acid might be considered as a component of metabolic syndrome. Context: Hyperuricemia is a common finding in patients with the metabolic syndrome. Recent studies indicated that hyperuricemia may be also a predictor of metabolic syndrome development.
Cardiovascular risk, insulin resistance, metabolic syndrome, Uric acid
Introduction The metabolic syndrome (MS) is commonly defined as the cluster of several abnormalities typically including central adiposity, abnormal blood pressure, dyslipidemia, and dysglycemia (NCEP, 2001). In particular, the diagnosis of MS is made when three or more of the risk determinants are present, in agreement to the National Cholesterol Education Program (NCEP)/Adult Treatment Panel III (ATP-III) criteria (NCEP, 2001) (Table 1). MS is often associated with the subsequent development of type 2 diabetes mellitus and cardiovascular disease (Ford et al., 2007). In addition to the well known NCEP/ATP-III definition, other conditions have been suggested to be associated with MS, including liver steatosis, increased oxidative stress, endothelial dysfunction, low grade systemic inflammation and hyperuricemia (Ford et al., 2007). With regard to this latter, elevated serum uric acid (SUA) levels are commonly seen in patients with MS, although its clinical meaning is still controversial and often underestimated (Borges et al., 2012). Traditionally, high levels of SUA in MS are, at least in part, attributed to insulin resistance. Concordantly, renal clearance of urate is inversely related to the degree of insulin resistance, while higher concentrations of insulin are known to reduce
Correspondence: Sara Ciarla, University of L’Aquila, Department of Life, Health and Environmental Sciences, L’Aquila, Italy. San Salvatore Hospital, Edificio Delta 6, Viale San Salvatore, 67100, Coppito, L’Aquila, Italy. Tel/Fax: +39 +862 368621. E-mail: [email protected]
History Received 19 March 2014 Revised 7 May 2014 Accepted 11 May 2014 Published online 10 June 2014
the renal excretion of urate (Facchini et al., 1991; Muscelli et al., 1996). Recent studies also suggest that elevated SUA levels can stimulate oxidative stress, endothelial dysfunction and vasoconstriction and, thereby, induce low grade systemic inflammation and favour the development of MS and cardiovascular disease (Baldwin et al., 2011). Concordant to all of these observations, a growing bulk of evidence indicates that elevated SUA levels might precede the development of MS (Fang and Alderman, 2000; Grassi et al., 2013; Ishizaka et al., 2005) and the 2012 European Society of Cardiology Guidelines on cardiovascular prevention indicate the measurement of serum uric acid levels as a component of the routine tests, i.e. to be assessed in all patients in order to stratify their cardiovascular risk (Perk et al., 2012). Despite these findings, a recent meta-analysis did not confirm a relationship between decrements in SUA levels and cardiovascular event rate (Savarese et al., 2013). Similarly, recent epidemiological data failed to demonstrate any correlation between SUA levels and cardiovascular mortality (Fang and Alderman, 2000). The reasons leading to these discrepancies are likely to be related with the study populations, including patients in primary and secondary prevention, often already on treatment with either allopurinol or febuxostat. The aim of the present study was to investigate the relationship among SUA levels, MS and insulin resistance in consecutive untreated patients with no confounding factors, i.e. previous diagnosis of hyperuricemia and/or gout; and who were in primary prevention, i.e. with no personal history of cardiovascular accidents.
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Table 1. Clinical identification of the metabolic syndrome according to National Cholesterol Education Program Adult Treatment Panel III criteria. Risk factor Waist circumference (cm) Blood pressure (mmHg) Fasting glucose (mg/dl) Triglycerides (mg/dl) HDL-cholesterol (mg/dl)
Defining level
102 cm in men, 88 cm in women 130/85 mmHg 100 mg/dl 150 mg/dl 40 mg/dl in men, 50 mg/dl in women
Archives of Physiology and Biochemistry Downloaded from informahealthcare.com by University of Waterloo on 12/05/14 For personal use only.
Patients and methods Patients A consecutive cohort of outpatients was observed at our Division of Internal Medicine – San Salvatore Hospital, L’Aquila, Italy – between September 2012 and December 2012. A total of 139 patients (67 males and 72 females) aged 18 to 70 years (mean age ± SD ¼ 47 ± 12 years) who underwent health examination was enrolled. Exclusion criteria consisted of an age 518 years or 470 years, secondary hypertension, calculated glomerular filtration rate 5 60 ml/min/1.73 m2, type 1 or 2 diabetes mellitus, use of any drug known to interfere with the uric acid metabolism (i.e. diuretics, ASA, losartan, postmenopausal hormone therapy), familiar or secondary dyslipidemia, known cardiovascular and/or cerebrovascular diseases, cancer, pregnancy and nursing, alcoholism, abuse of drugs and psychic alterations.
Methods Demographic and clinical data were collected with standardized interview sheets. Body weight (kg) and standing height (m) were measured using a scale and a stadiometer after the subjects were fasting overnight and wearing only light underwear. Body mass index (BMI) was then calculated as weight in kilograms divided by the square of height in metres (WHO, 1995). Waist circumference (cm) was measured using a steel measuring tape at the level of the iliac crest at the end of a normal expiration (USDHHS, 1996). Blood pressure measurements (systolic blood pressure, SBP; diastolic blood pressure, DBP) were performed twice in all subjects, in the sitting position, using a mercury sphygmomanometer and the auscultatory technique, according to European Society of Hypertension Recommendations (Mancia et al., 2013). After the above procedures, blood samples were collected in the morning after 8–12 hours at fast. Serum low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglyceride, high-sensitive C-reactive protein (hs-CRP), creatinine, SUA, fasting glucose and insulin levels were determined by standard laboratory methods. We used the Modification of Diet in Renal Disease (MDRD) formula to estimate glomerular filtration rate (eGFR) expressed in ml/ min/1.73 m2. Insulin resistance was estimated by the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) equation, calculated as [(fasting plasma glucose, mmol/l) (fasting serum insulin, mU/ml)]/22,5 (Matthews et al., 1985).
Statistical analysis All statistical analysis were performed by the Statistical Analysis System (SAS) software (SAS Institute Inc., Cary, NC, USA). Unless otherwise stated, all results are expressed as mean ± standard deviation. The distribution of values for each variable was analysed by the Shapiro-Wilk normality test. In order to non-normal distribution of variables, analysis of variance was performed with non-parametric ANOVA (Kruskal-Wallis test). The posthoc analysis was made according to Conover. The Spearman coefficient was used for linear correlations between variables. Multiple linear regression after logarithmic transformation of variables was used to evaluate the relative weight and persistence of the relationship among variables. A p value 50.05 was considered as statistically significant for all analysis.
Results Sixty-two patients (49 ± 10 years) resulted affected by MS (NCEP/ATP-III criteria (NCEP, 2001)), while 77 patients (42 ± 12 years) had no MS. The whole study population was divided into six groups according to the presence of 0 (healthy controls, n ¼ 25), 1 (n ¼ 29), 2 (n ¼ 23), 3 (n ¼ 27), 4 (n ¼ 24) or 5 (n ¼ 11) MS components. Patient’s data are showed in Table 2. SUA levels ranged from 2.1 mg/dl to 11.5 mg/dl (mean ± SD ¼ 5.46 ± 1.80 mg/dl), being significantly higher between males (6.17 ± 1.63 mg/dl) than females (4.80 ± 1.71 mg/dl) (p50.0001). Mean SUA concentrations were also significantly higher in patients with (6.19 ± 1.84 mg/dl) than without MS (4.88 ± 1.55 mg/dl) (p50.0001). Mean SUA levels increased gradually with the number of MS components from 0 (4.10 ± 1.23 mg/dl), 1 (5.06 ± 1.54 mg/dl), 2 (5.48 ± 1.57 mg/dl), 3 (5.67 ± 1.31 mg/dl), 4 (6.34 ± 2.26 mg/dl) or 5 (7.09 ± 1.63 mg/dl) (p for trend 50.0001). In the whole study population, SUA directly correlated with serum creatinine levels (p50.0001, r ¼ 0.44), body weight (p50.0001, r ¼ 0.46), BMI (p ¼ 0.0002, r ¼ 0.31), waist circumference (p50.0001, r ¼ 0.40), fasting serum glucose (p ¼ 0.003, r ¼ 0.25) and insulin levels (p50.0001, r ¼ 0.42), HOMA-IR (p50.0001, r ¼ 0.42), serum triglyceride (p50.0001, r ¼ 0.45) and hs-CRP levels (p50.0001, r ¼ 0.37); and inversely correlated with serum HDL-cholesterol concentrations (p50.0001, r ¼ 0.51). The multiple regression analysis in the entire population SUA levels directly correlated with HOMA-IR (p ¼ 0.01), serum triglyceride (p ¼ 0.04), creatinine (p ¼ 0.02) and hs-CRP levels (p ¼ 0.03) and inversely correlated with serum HDL-cholesterol concentrations (p ¼ 0.005). HOMAIR, serum triglyceride, creatinine, hs-CRP and HDL-cholesterol levels were the main determinants of SUA levels, explaining about 40.4% of its variance (r2 ¼ 0.404, p50.001).
Discussion The current study confirms that SUA levels are higher in subjects with than without MS. In particular, SUA levels increased gradually in the study population, according to the
Serum uric acid levels and metabolic syndrome
DOI: 10.3109/13813455.2014.924145
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Table 2. Clinical and laboratory characteristics in the study population (data are expressed as mean ± standard deviation, median, range).
Age (years) Weight (kg)
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BMI (kg/m2) Waist circumference (cm) SBP (mmHg) DBP (mmHg) Creatinine (mg/dl) eGFR (ml/min/1.73m2) Uric acid (mg/dl) Blood-glucose (mg/dl) Insulinemia (mU/ml) HOMA-IR Triglycerides mg/dl) LDL-cholesterol (mg/dl) HDL-cholesterol (mg/dl) hs-CRP (mg/dl)
Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range Mean ± DS Median Range
0 MS Component (n ¼ 25, 10 Men)
1 MS Component (n ¼ 29, 17 Men)
2 MS Components (n ¼ 23, 12 Men)
3 MS Components (n ¼ 27, 11 Men)
4 MS Components (n ¼ 24, 12 Men)
5 MS Components (n ¼ 11, 5 Men)
42.84 ± 12.48 39 21.00–73.00 64.52 ± 12.18 65 42.00–87.00 23.02 ± 2.39 23.44 18.00–26.90 82.44 ± 9.13 84 60.00–98.00 117.80 ± 9.25 120 100.00–130.00 74.20 ± 6.56 75 60.00–85.00 0.77 ± 0.14 0.72 0.56–1.10 105.12 ± 20.87 109 57.00–141.00 4.10 ± 1.24 3.9 2.10–6.10 81.72 ± 9.27 82 63.00–98.00 7.10 ± 2.81 6.93 2.68–13.12 1.44 ± 0.62 1.5 0.40–2.51 75.72 ± 25.35 76 39.00–152.00 91.60 ± 31.78 98 20.00–140.00 57.20 ± 12.97 56 41.00–92.00 0.10 ± 0.12 0.04 0.01–0.50
47.41 ± 13.63 51 25.00–70.00 78.10 ± 14.33 78 56.00–110.00 27.26 ± 5.04 26 20.80–46.50 93.24 ± 11.93 92 70.00–121.00 130.66 ± 14.51 130 105.00–155.00 80.14 ± 9.89 80 60.00–99.00 0.78 ± 0.13 0.78 0.58–1.12 102.93 ± 21.57 108 64.00–154.00 5.07 ± 1.54 5 2.90–9.50 90.07 ± 18.58 89 70.00–173.00 9.92 ± 5.28 8.6 1.70–29.16 2.22 ± 1.32 1.8 0.30–7.05 103.55 ± 30.43 96 53.00–160.00 125.83 ± 29.08 117 64.00–195.00 55.79 ± 17.40 51 26.00–103.00 0.28 ± 0.36 0.2 0.01–1.63
47.08 ± 14.84 48 19.00–70.00 80.00 ± 17.67 78 42.00–136.00 28.76 ± 6.49 27 18.20–49.50 99.74 ± 16.79 98 70.00–150.00 133.00 ± 13.37 133 110.00–160.00 83.26 ± 11.07 85 66.00–105.00 0.79 ± 0.13 0.74 0.60–1.02 99.65 ± 21.11 96 65.00–138.00 5.49 ± 1.57 5.2 3.40–9.00 87.35 ± 13.74 86 57.00–126.00 11.26 ± 4.84 11.89 3.64–22.62 2.49 ± 1.20 2.6 0.51–4.74 137.17 ± 42.58 128 70.00–222.00 129.30 ± 34.89 131 76.00–189.00 49.96 ± 15.93 48 28.00–110.00 0.46 ± 0.71 0.25 0.02–3.50
49.51 ± 11.90 50 27.00–70.00 85.15 ± 16.39 80 54.00–119.00 29.39 ± 4.86 29.6 21.00–43.50 103.19 ± 12.37 102 84.00–131.00 140.74 ± 19.10 140 100.00–180.00 85.59 ± 12.41 85 65.00–110.00 0.79 ± 0.14 0.75 0.55–1.13 99.69 ± 22.66 92.5 69.00–155.00 5.67 ± 1.31 5.5 3.50–8.60 91.89 ± 18.17 88 57.00–136.00 15.54 ± 11.86 12 4.00–64.73 3.66 ± 3.36 3.08 0.80–18.38 158.44 ± 107.26 151 60.00–571.00 132.63 ± 42.72 130 44.00–216.00 41.78 ± 11.17 42 23.00–70.00 0.58 ± 0.55 0.38 0.02–2.00
48.5 ± 10.53 49.5 28.00–70.00 94.04 ± 13.50 95.5 71.00–133.00 33.34 ± 4.75 32.5 27.40–47.00 108.42 ± 9.42 107 91.00–133.00 142.63 ± 13.99 145 109.00–167.00 89.13 ± 10.52 90 68.00–110.00 1.04 ± 1.27 0.8 0.55–7.00 108.29 ± 25.43 106 61.00–153.00 6.35 ± 2.26 5.7 2.40–11.50 107.08 ± 35.61 98.5 68.00–205.00 17.79 ± 13.73 14.85 5.90–77.33 5.27 ± 6.20 3.51 0.99–32.06 195.88 ± 95.08 188 69.00–419.00 130.50 ± 34.33 128 65.00–191.00 35.92 ± 10.12 37 10.00–53.00 0.67 ± 0.65 0.4 0.07–2.50
51.81 ± 9.72 53 31.00–62.00 102.00 ± 15.76 99 86.00–133.00 36.40 ± 5.83 34.5 30.10–47.00 115.82 ± 14.38 111 96.00–147.00 136.55 ± 9.43 140 118.00–150.00 89.91 ± 8.28 90 78.00–100.00 0.82 ± 0.16 0.79 0.60–1.10 104.82 ± 18.61 103 78.00–136.00 7.09 ± 1.64 6.8 5.20–10.60 123.45 ± 20.15 116 104.00–168.00 31.80 ± 18.38 26.4 10.76–77.33 10.33 ± 8.10 7.43 2.58–32.06 306.55 ± 182.84 255 194.00–826.00 121.73 ± 26.74 121 80.00–165.00 34.55 ± 7.34 38 20.00–45.00 0.66 ± 0.68 0.37 0.05–2.50
MS, metabolic syndrome; DS, standard deviation; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, glomerular filtration rate (estimated eGFR using Modification of Diet in Renal Disease formula); HOMA-IR, homeostasis model assessment of insulin resistance; LDL-cholesterol, low-density lipoprotein cholesterol; HDL-cholesterol, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein
number of MS components. Further SUA strongly correlated with HOMA-IR, triglyceride and HDL-cholesterol concentrations. Similar findings have been demonstrated in other studies (Ishizaka et al., 2005; Shi-Dou et al., 2006). There are several potential mechanisms that could account for the elevated concentration of SUA in MS. The renal clearance of urate is inversely related to the degree of insulin resistance (Fachinni et al., 1991; Muscelli et al., 1996). Concordantly, in our patients SUA directly correlated with
both fasting insulin levels and HOMA-IR. On the other hand, hyperuricemia could concur to the pro-inflammatory endocrine imbalance, particularly at the adipose tissue level, and thereby favours the onset of insulin resistance (Baldwin et al., 2011). According to this, in our MS patients SUA directly correlated with both BMI and hs-CRP levels. Thus, an elevated SUA concentration might represent either a consequence or a concomitant cause of insulin resistance. In our study, SUA levels were also higher in males than females. The difference in SUA levels between genders has
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been often described and is likely due to the increased renal urate clearance induced by estrogen in woman, particularly before menopause (Mumford et al., 2013). Our study has some limitations. First, it is known that hypoxia in obstructive sleep apnea syndrome (OSAS), at least in obese patients, is a potential cause of increased SUA levels (Saito et al., 2002). Unfortunately, we have no data about OSAS and are then unable to speculate on this matter. Secondly, the sample size was relatively small. Finally, the lack of longitudinal data does not allow one to estimate the relationship between SUA levels and cardiovascular events. Thus, we are also unable to assess the potential contribution of SUA to the development of cardiovascular disease. Despite these limitations, the current study strongly suggests that uric acid has a marked relation with HOMAIR and fasting insulin levels. Therefore, the same study is extremely relevant to emphasize the role of the prevention of insulin resistance and related conditions, i.e. obesity. In conclusion, hyperuricemia was strongly associated with MS. In our study cohort, the relationship between SUA levels and various metabolic parameters strongly suggests that hyperuricemia might be considered as a component of MS. Based on our data, whether SUA is an innocent bystander, a partner or a contributor to MS remains unanswered.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
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