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Print version ISSN 1807-5932

Clinics vol.66 no.9 São Paulo  2011 



Prevalence of metabolic syndrome and related factors in Taiwanese high-tech industry workers



Tzung-Yi TsaiI;Jung-Feng ChengII;Yu-Min LaiIII,IV

I Department of Research, Buddhist Dalin Tzu Chi General Hospital, Taiwan
II Department of Nursing, Buddhist Dalin Tzu Chi General Hospital, Taiwan
Department of Family Medicine, Buddhist Dalin Tzu Chi General Hospital, Taiwan
IVSchool of Medicine, Tzu Chi University, Hualien, Taiwan




OBJECTIVES: In light of the increasing number of high-tech industry workers and the differences in their working conditions compared to those of the general population, the health status of these workers merits serious attention. This study aimed to explore the prevalence of metabolic syndrome and its correlates among Taiwanese high-tech industry workers.
METHODS: This cross-sectional study included 4,666 workers who participated in labor health examinations at a hospital in southern Taiwan in 2008. Participants with metabolic syndrome were defined using the criteria proposed by the Taiwan National Department of Health in 2007. Factors associated with metabolic syndrome were determined using multiple logistic regression analysis.
RESULTS: The overall prevalence of metabolic syndrome was 8.2%, and the prevalence was higher in men than in women (14.0% vs. 2.3%, p<0.01). Male gender, advanced age, elevated white blood count, and elevated levels of blood biochemistry markers, such as alanine aminotransferase and uric acid, can independently predict metabolic syndrome.
CONCLUSIONS: The prevalence of metabolic syndrome among high-tech industry workers is lower than in the general population. Our study's findings may facilitate early health assessments and the provision of proper workplace health promotion programs to reduce the risks faced by high-risk workers.

Keywords: High-tech industry; Metabolic syndrome; Workplace health promotion.




As a result of economic growth and associated sociodemographic shifts, changes in lifestyle and diet have led to an elevated risk with respect to chronic diseases, especially cardiovascular disease. According to the World Health Organization (WHO), cardiovascular disease is the leading cause of death worldwide, claiming 17.5 million victims yearly.1 This high mortality r ate creates an enormous financial burden. In the European Union, the yearly direct and indirect economic cost of cardiovascular disease is as high as 169 billion EUR.2 In contrast, after reviewing the top ten caus es of mortality in Taiwan, we found that chronic diseases had replaced infectious diseases as the main cause of death. For example, according to statistics reports from the Taiwan National Department of Health in 2008,3 the mortality attributed to metabolic abnormalities (29.5%), including cerebrovascular disease, heart disease, diabetes mellitus, hypertension, and pyelonephritis, had surpassed cancer mortality (27.3%), revealing that metabolic abnorm alities have become a major health challenge.

Given its threat to the well-being of humans, a syndrome related to insulin resistance abnormalities was first pro posed by Reaven in 1988.4 Subsequently, in 1998, the WHO introduc ed the first internationally recognized term for met abolic abnormalities, metabolic syndrome (MS), which is characte rized by a core set of disorders, including glucose into lerance, central obesity, dyslipidemia, and hypertension co-occur ring within an individual.5 Although there is still some d ebate as to the precise definition of MS, its prev alence around the world has been estimated at approximately 7% to 58% according to the Adult Treatment Panel III definition, and these figures were still rising when the criteria were specified by the WHO.6

Recently, investigators have reported on the challenge that MS poses for human health. For example, MS is not only an indication of the likelihood of developing coronary heart disease or type II diabetes but is also associated with a threefold greater risk of cardiovascular death, as compared to those without the disorder.5 Additionally, MS is related to cerebral atrophy, which may trigger depression and cognitive impairment.7,8 Most recently, studies on the

correlati on between malignancy and MS have shown that patients with MS have a 1.6-fold higher risk of death from cancer than those without MS.9 Therefore, clarifying the factors related to MS and to identify high-risk cases early may be of utmost importance for public health.

Workers are an important part of any institution. Since the promulgation of the Ottawa Charter for Health Promotion by the WHO in 1986, global support for work place health promotion has encouraged employers to hold health promotion activities to ensure employee health.10 With increasing competition in the global market and the rapid development of technology, all countries (including Taiwan) seek to foster growth in high-tech industries. Facilitated by support from the government, high-tech products comprised 71.2% of all exports from Taiwan in 2006 with a value of US$159.5 billion.11 Taiwan is a pivotal site for high-tech industry development in the world, but increasing demand for high-tech products may increase the already heavy workload for workers in this industry. Therefore, the health status of high-tech industry workers requires immediate attention. Many previous studies have focused on musculoskeletal discomfort,12 workplace stress,13 skin disease,14 and lumbar pain,15 among other issues, but there is a relative paucity of data pertaining to metaboli c abnormalities in the workplace. Most impor tantly, the medical expenses related to MS are considerable and thus cannot be overlooked. Goetzel et al.16 found that obesity poses a burden of US$644 per worker on companies, due to medical expenses and loss of productivity. Moreover, this cost rises to US$3,719 with the occurrence of MS,17 which may have a major impact on the fiscal performance of the company. Therefore, our study aimed to understand the prevalence of MS and related factors in high-tech industry workers, which should facilitate the development of strategies for promoting appropriate workplace health.



Research design and participants

This study was cross-sectional. The subjects were workers from a company in the integrated circuit industry; the company contracted a hospital in Taiwan for health examination services in 2008. A total of 4,666 workers providing informed consent were included in this study, and the study was reviewed and approved by the Institutional Review Board of the target hospital.

Data characteristics and diagnostic criteria

Analytical data were obtained from the health examination database. These data included gender, age, height, body weight, blood pressure, waist circumference, and blood biochemistry markers (i.e., white blood cell count (WBC), hemoglobin concentration, fasting blood glucose levels, high-density lipoprotein cholesterol (HDL-C), triglycerides, uric acid (UA), creatinine, aspartate aminotransferase (AST), and alanine aminotransferase (ALT)). The body mass index (BMI) was calculated by dividing weight (in kg) by height (in m2). Systolic and diastolic blood pressures were measured using an automatic sphygmomanometer (UDEX-IIa, UEDA Corp., Tokyo, Japan) and recorded in the seated position after participants had rested for at least 5 min. Waist circumference was measured with a heavy-duty inelastic plastic fiber tape measure to the nearest 0.5 cm, while the subject stood balanced on both feet with the feet touching each other and both arms hanging freely. Hemoglobin concentration, ALT, AST, triglycerides, fasting blood glucose, UA, WBC, and HDL-C were evaluated by automatic bio-assays (Olympus AU-640, Tokyo, Japan).

MS was defined as the presence of three or more of the followin g five criteria proposed by the Taiwan National Health Department:18 (1) fasting blood glucose ≥$100 mg/dl or self-reported diabetes mellitus; (2) blood pressure ≥$130/ 85 mmHg or self-reported hypertension; (3) HDL-C cholesterol <40 mg/dl in men or <50 mg/dl in women; (4) triglycerides ≥$150 mg/dl; and (5) waist circumference ≥$90 cm in men or ≥$80 cm in women.

Statistical analysis

Descriptive and inferential analyses were conducted in accordance with the study's aims and the nature of the variables. For descriptive analysis, subjects were grouped according to the presence of MS. Differences in various factors between groups are expressed with averages, standard deviations (SDs), and percentages. Besides gender, which is shown as a percentage, other variables with continuous distributions are expressed as averages with SD. For inferential analysis, t-tests and chi-square tests were used to explore the relationship between analytical variables and MS. Multiple logistic regression analysis with the simultaneous entry method was further used to determine risk factors and their adjusted odds ratios (AORs). All analyses were conducted with Statistical Package for Social Sciences (SPSS) version 15.0 software (SPSS, Inc., Chicago, IL), and p<0.05 was considered significant.



The 4,666 high-tech industry workers enrolled in this study included 2,344 men and 2,322 women. Among subjects, the overall prevalence of MS was 8.2% (382/ 4,666) with 14.0% (328/2,344) in men and 2.3% (54/2,322) in women, revealing a higher MS prevalence in men (p,0.01). Based o n the 2007 population census published by the Department of Health,19 the age-standardized prevalence was 5.9%, which was also higher in men than in women (10.1% vs. 1.7%, p<0.01). Additionally, men also showed higher BMIs, WBCs, liver enzyme levels, creatinine, UA, and hemoglobin, as compared to women (p<0.01). Subjects with MS averaged 33.5 years in age, as opposed to 30.2 years for those without MS. On average, workers with MS had significantly higher BMIs and abnormal blood samples, as compared to those without MS (Table 1).

Our analysis also showed that the most common metabolic abnormalities among men and women with MS was higher waist circumference, which was followed by irregular blood p ressure and hypertriglyceridemia; these disorders had prevalences of 82.0%, 81.4%, and 73.2% in males and 98.2%, 88.9%, and 64.8% in females, respectively (Table 2).



Multivariate analysis revealed that men had an AOR of 3.94 (95% confidence interval (CI) = 2.2926.78) for developing MS, as compared to women. A one-year increase in age was associated with a 6% (95% CI = 1.04-1.09) increase in the risk of MS. An increase in BMI of one raised the AOR for MS by 2% (95% CI = 1.0121.02). Regarding blood biochemistry markers, an increase of one unit of ALT increased the AOR for MS by 30% (95% CI = 1.2621.35). An increase of one unit of UA or WBC was associated with an increase of 14% (95% CI = 1.0321.27) and 12% (95% CI = 1.0421.19), respectively, in the risk of being diagnosed with MS (Table 3).




The overall MS prevalence was 8.2% (14.0% in men and 2.3% in women). Because the average age of the subjects, who ranged in age from 31 to 40 years, was younger than in most other epidemiological studies, it is difficult to directly compare our results with MS prevalence reported else where. However, as compared to the prevalence in similarly aged m en and women (10.1%–25.8% and 2.1%–19.7%, respectively20-22), the MS prevalence among high-tech industry workers was lower than in the general population. This difference may be attributed to the healthy worker effect: these workers not only must demonstrate a better- than-average health status to qualify for work but also are screened by health examinations before being allowed to enter the high-tech industry.

We found that the most common manifestations of MS in both men and women were high waist circumference followed by high blood pressure and hypertriglyceridemia. However, Pan23 found that blood pressure, triglyceride, and HDL-C disorders were the most common metabolic abnormalities among male and female adults in Taiwan. Another study of 1,562 Taiwanese teenagers indicated that triglycerides, HDL-C, and waist circumference were the most common abnormal indicators in male adolescents, as compare d to HDL-C, blood pressure, and waist circumfer ence in female adolescents.24 Findings based on the same ethnic group, therefore, may vary due to age discrepancies with respect to the leading types of metabolic abnormalities (one third of Pan's subjects were aged 45 years or above). Despite several studies showing a significant correlation between age and predisposition for MS,21-22 further research is needed to elucidate whether the leading metabolic abnormalities vary with age.

Men were at a 3.47-fold greater risk of MS than women, which is in line with previous studies.22 Two reasons may account for this phenomenon. First, men are less aware of self-care than women. For instance, not only do fewer women smoke and drink, but they are more prone to seek medical counsel or an examination in the event of physical discomfort. Thus, the risks for chronic diseases, such as hyp ertension and diabetes, are usually lower in women than in men.225 Second, this study was mostly conducted on you nger subjects, and younger women may benefit from high er estrogen levels, which decrease serum low-density lipoprotein-cholesterol (LDL-C) and curb coronary thrombosis and atherosclerosis by regulating vascular smooth muscle and endothelial cells.

Our findings also show that age was positively correlated with the risk of MS, which is in line with previous studies. 20-21 It can be inferred that, with age, blood vessels gradually lose elast icity and gain resistance, slowing blood flow. Moreover, with po or circulation, fat is prone to accumulate in the abd omen and release free fatty acids into the serum, leading to higher insulin resistance, elevated serum triglyceride levels, increased small dense LDL-C levels, and, consequently, a greater risk of MS.26 This study also showed that a higher BMI was associated with a greater AOR for developing MS, which confirms a previous report.21 BMI can be used to assess body fat content, which serves as an indicator of obesity. In cases of obesity, adipose cells release free fatty acids and tumor necrosis factor-a (TNF-α), which blocks phosphatidylinosi tide-3-ki nase-dependent signal transduction pathways and reduces glucose uptake in the liver and skeletal muscles.6,27 To compensate, pancreatic β cells are induced to secrete excessive insulin, leading to hyperglycemia or type II diabetes.

We discovered that ALT can better assess the risk of MS than AST, which is in line with both cross-sectional and cohort studies.28-29 This finding stems from the storage of ALT in hepatic cells; therefore, ALT is a more effective marker for detecting pathogenic hepatic fatty changes caused by obesity, as compared to AST.29 Two reasons may explain the positive relationship between ALT and the risk of MS. First, abnormally elevated ALT is often found in people under tremendous workplace pressure or extreme fatigue.30 High psychological pressure may activate the sympath etic system to raise arterial blood pressure and reduce renal blood flow.31 Consequently, the patient develops vasculitic reactions and elevated serum UA levels, increasing the risk of MS. Second, elevated ALT can also indicate abnormal liver function caused by fatty liver.28 Once a patient has developed fatty liver, fat accumulation has usu ally already occurred in the muscle and other organs, such as the pancreas. As these tissues release free fatty acids that inhibit the insulin-based regulation of the uptake and utilization of glucose, metabolic abnormalities subsequently emerge.27

This study found a positive correlation between WBC and the risk of MS, which was in line with previous literature reports. A higher WBC may be related to bacterial infection or vasculitis caused by hyperglycemia and dyslipid emia. In an inflammatory state, vascular endothelial cells secrete proinflammatory cytokines, such as interleukin 6 or TNF-α, which can activate and attract massive numbers of white blood cells to the damaged region within the lumen of the vessel.33 Following infiltration into the tunica media, white blood cells absorb oxidized LDL-C and become foam cells, which may greatly increase the risk of thromboembo lism. Additionally, Rho kinase secreted by white blood cells can serve as another potential pathogenic mechanism. Liu et al.32 have found that Rho kinase can block insulin signaling by stimulating the phosphorylation of insulin receptor substrate-1. This action prevents the translocation of glucose to the cell membrane by glucose transporter 4, thereby leading to insulin resistance.

Our study revealed that workers with MS have a higher risk of elevated UA, which is in accordance with a previous report.34 The mechanism involved may be related to the ability of high levels of UA to inhibit the production of nitric oxide (NO) by endo thelial cells. NO can serve as an inhibitor of thrombosis and inflammatory reactions, further preventi ng the growth and transfer of vascular smooth muscle cells.35 As for hemoglobin levels, we discovered that hemoglo bin was of borderline significance. Because the specific biological mechanisms by which hemoglobin levels increase the risk of MS remain unclear, further study is needed to clarify the effect of hemoglobin levels on MS, especially among diverse participants.

The results from this study should be interpreted with the following limitations in mind. First, subjects were drawn from a single company in southern Taiwan, and, therefore, the inferences drawn from the results are not completely gene ralizable. More diverse samples collected by a nationwide survey or random sampling should be considered in future research. Second, as a cross-sectional study, we cannot infer causality from our findings. A longitudinal study design is needed to investigate any causal relationships among the factors included in this study. Third, data on potential factors, such as smoking, alcohol, education, exercise habits, family histo ry, and worksite pressure were not collected in this study; lack of information about these factors may have caused some inferential error. Future studies are needed to understand the impact of the abovementioned confounding variables on MS. Moreover, issues related to the medical costs induced by MS also warrant further evaluation. Despite these methodological concerns, to our knowledge, this was the first evidence-based study to estimate the prevalence of MS in Taiwanese high-tech industry workers. It also involved an analysis of corresponding health check-up data, which can be useful in assessing the risk factors for MS because the data were obtained from standardized laboratory tests.



This study explored the prevalence of MS and related factors among high-tech industry workers. We found that factors contributing to a high risk of MS included being male and older, as well as having a higher BMI, increased ALT, elevated UA, and higher WBC. If our study is replicated in other occupational settings, the results may have significant economic and public-health implications for other shift-work occupational groups, such as the military, medical service personnel, and police officers. We believe that our findings support the need to improve workplace health promotion in the high-tech industry. For example, appropriate on-site diet and exercise programs aimed at high-tech industry workers should be implemented. In addition, occupational managers should actively offer in-service education concerning MS prevention to workers and monitor their physical and mental health, especially in high-risk cases. In summary, the identification of at-risk workers and disease management programs addressing MS-related factors are of great importance in employer-based interventions.



This study was supported by grants from the Buddhist Dalin Tzu Chi General Hospital.



1. World Health Organization. Cardiovascular Diseases. Available online: Accessed 2010 Apr 25.         [ Links ]

2. Leal J, Luengo-Fernández R, Gray A, Petersen S, Rayner M. Economic burden of cardiovascular diseases in the enlarged European Union. Eur Heart J. 2006;27:1610-9, doi: 10.1093/eurheartj/ehi733.         [ Links ]

3. D epartment of Health. 2008 Statistics of Cause of Death. Available online: =3&doc_no=73104. Accessed 2010 Jun 20.         [ Links ]

4. Reaven GM. Banting lecture 1988.Role of insulin resistance in human disease. Diabetes. 1988;37:1595-607, doi: 10.2337/diabetes.37.12.1595.         [ Links ]

5. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, et al. Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol. 2007;49:403-14, doi: 10.1016/j.jacc.2006.09.032.         [ Links ]

6. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al. The metabolic syndrome. Endocr Rev. 2008;29:777-822, doi: 10. 1210/er.2008-0024.         [ Links ]

7. Koponen H, Jokelainen J, Keinänen-Kiukaanniemi S, Kumpusalo E, Vanhala M. Metabolic syndrome predisposes to depressive symptoms: a population -based 7-year follow-up study. J Clin Psychiatry. 2008;69:178 82, doi: 10.4088/JCP.v69n0202.         [ Links ]

8. Yaffe K, Weston AL, Blackwell T, Krueger KA. The metabolic syndrome and development of cognitive impairment among older women. Arch Neurol. 2009;66:324-8, doi: 10.1001/archneurol.2008.566.         [ Links ]

9. Jaggers JR, Sui X, Hooker SP, LaMonte MJ, Matthews CE, Hand GA, et al. Metabolic syndrome and risk of cancer mortality in men. Eur J Cancer. 2009;45:1831-8.         [ Links ]

10. McMahon A, Kelleher CC, Helly G, Duffy E. Evaluation of a workplace cardiovascular health promotion programme in the Republic of Ireland. Health Promot Int. 2002;17:297-308, doi: 10.1093/heapro/17.4.297.         [ Links ]

11. Directorate General of Budget, Accounting and Statistics, ROC. National Statistics. Available online: 77316232871.pdf. Accessed 2010 Jun 8.         [ Links ]

12. Lin RT, Chan CC. Effectiveness of workstation design on reducing musculoskeletal risk factors and symptoms among semiconductor fabrication room workers. Int J Ind Ergon. 2007;37:35-42, doi: 10.1016/j. ergon.2006.09.015.         [ Links ]

13. Huang C. Y., Su, S. B., Guo, H. R., & Hsu, S. Y. Using Karasek's model to evaluate work related stress among male rotating shift workers in the high-tech industry in Taiwan. Chin J Occup Med. 2009;16:109-17.         [ Links ]

14. Shiao JSC, Sheu HM, Chen CJ, Tsai PJ, Guo YL. Prevalence and risk factors of occupational hand dermatoses in electronics workers. Toxicol Ind Health. 2004;20:1-7, doi: 10.1191/0748233704th193oa.         [ Links ]

15. Hsu WH, Wang MJ. Physical discomfort among visual display terminal users in a semiconductor manufacturing company: a study of prevalence and relation to psychosocial and physical/ergonomic factors. AIHA J. 2003;64:276-82, doi: 10.1080/15428110308984818.         [ Links ]

16. Goetzel RZ, Gibson TB, Short ME, Chu BC, Waddell J, Bowen J, et al. A multi-worksite analysis of the relationships among body mass index, medical utilization, and worker productivity. J Occup Environ Med. 2010;52:S52-8, doi: 10.1097/JOM.0b013e3181c95b84.         [ Links ]

17. Schultz AB, Edington DW. Metabolic syndrome in a workplace: prevalence, co-morbidities, and economic impact. Metab Syndr Relat Disord. 2009;7:459-68, doi: 10.1089/met.2009.0008.         [ Links ]

18. National Department of Health. The Definition of the Metabolic Syndrome in Adults. Available online:;jsessionid=EB0141BDFD1C5CCCE6369 69C323CEE3C?p_No=200711816324963WA3X+. Accessed 2009 Aug 20.         [ Links ]

19. National Department of Health. Health Statistics Indicators in 2008. Available online: Accessed 2008 December 5.         [ Links ]

20. Chuang SY, Chen CH, Chou P. Prevalence of metabolic syndrome in a large health check-up populations in Taiwan. J Chin Med Assoc. 2004;67:611-20.         [ Links ]

21. Hwang LC, Bai CH, Chen CJ. Prevalence of obesity and metabolic syndrome in Taiwan. J Formos Med Assoc. 2006;105:626-35, doi: 10.1016/ S0929-6646(09)60161-3.         [ Links ]

22. Lohsoonthorn V, Lertmaharit S, Williams MA. Prevalence of metabolic syndrome among professional and office workers in Bangkok, Thailand. J Med Assoc Thai. 2007;90:1908-15.         [ Links ]

23. Pan WP. Metabolic syndrome-an important but complex disease entity f or Asians. Acta Cardiol Sin. 2002;18:24-6.         [ Links ]

24. Chin HC, Chu NF, Shen MH, Wu DM. Prevalence of metabolic syndrome among junior high school students in Taipei. Taiwan J Fam Med. 2007;17:27-37.         [ Links ]

25. Minh HV, Byass P, Chuc NTK, Wall S. Gender differences in prevalence and socioeconomic determinants of hypertension: findings from the WHO STEPs survey in a rural community of Vietnam. J Hum Hypertens. 2 006;20:109-15, doi: 10.1038/sj.jhh.1001942.         [ Links ]

26. Ai M, Otokozawa S, Asztalos BF, Ito Y, Nakajima K, White CC, et al. Small dense LDL cholesterol and coronary heart disease: Results from the Framingham Offspring study. Clin Chem. 2010;56:967-76, doi: 10. 1373/clinc hem.2009.137489.         [ Links ]

27. Arner P. Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab Res Rev. 2002;18:S5-9, doi: 10.1002/dmrr.254.         [ Links ]

28. Perera S, Lohsoonthorn V, Jiamjarasrangsi W, Lertmaharit S, Williams MA. Asso ciation between elevated liver enzymes and metabolic syndrome among Thai adults. Diabetes Metab Clin Res Rev. 2008;2:171- 8, doi: 10.1016/j.dsx.2008.04.012.         [ Links ]

29. Goessling W, Massaro JM, Vasan RS, D9Agostino RB Sr, Ellison RC, Fox CS. Aminotransferase levels and 20-year risk of metabolic syndrome, diabetes, and cardiovascular disease. Gastroenterology. 2008;135:19354 4, doi: 10.1053/j.gastro.2008.09.018.         [ Links ]

30. Lin YC, Chen JD, Chen CJ. Abnormal liver function and central obesity associate with work-related fatigue among the Taiwanese workers. World J Gastroenterol. 2008;14:6541-5.         [ Links ]

31. Mancia GA, Bousquet PB, Elghozi JL, Esler MD, Grassi GA, Julius SE, et al. The sympathetic nervous system and the metabolic syndrome. J Hypertens. 2007;25:909-20, doi: 10.1097/HJH.0b013e328048d004.         [ Links ]

32. Liu PY, Chen JH, Lin LJ, Liao JK. Increased rho kinase activity in a Taiwanese population with metabolic syndrome. J Am Coll Cardiol. 2007;49:1619-24, doi: 10.1016/j.jacc.2006.12.043.         [ Links ]

33. Oda E, Kawai R. Comparison between high-sensitivity c-reactive protein (hs-CRP) and white blood cell count (WBC) as an inflammatory component of metabolic syndrome in Japanese. Int ern Med. 2010;49:117-24, doi: 10.2169/internalmedicine.49.2670.         [ Links ]

34. Sui X, Church TS, Meriwether RA, Lobelo F, Blair SN. Uric acid and the developme nt of metabolic syndrome in women and men. Metabolism. 2008;57:845-52, doi: 10.1016/j.metabol.2008.01.030.         [ Links ]

35. Maxwell AJ, Bruinsma KA. Uric acid is closely linked to vascular nitric oxide activity : Evidence for mechanism of association with cardiovascular disease. J Am Coll Cardiol. 2001;38:1850-8, doi: 10.1016/S07351097(01)01643-6.         [ Links ]



Received for publication on March 28, 2011; First review completed on May 2, 2011; Accepted for publication on May 16, 2011



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