Meta-analysis for the Relationship Between Hyperuricemia and CVD Incidence

doi:10.21203/rs.3.rs-936526/v1

Download PDF

Research Article

Meta-analysis for the Relationship Between Hyperuricemia and CVD Incidence

https://doi.org/10.21203/rs.3.rs-936526/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

BACKGROUND: Uric acid was once considered an effective endogenous antioxidant, but now more and more evidence shows that it may play a significant role in the pathophysiology of cardiovascular diseases.

OBJECTIVES: It has not been clear that UA is a sign of poor prognosis or a risk factor for CVD. Our aim is to figure out the exact relationship between CVD and uric acid.

METHODS: We studied 3356 publications in the past 44 years through MEDLINE, EMBASE, and Cochrane library searches, and selected 22 studies that met our inclusion criteria.

RESULTS: The meta-analysis showed that hyperuricemia was associated with an increased risk of death from CVD (RR=1.37; 95% CI:1.29-1.45; I2=31.4%, P=0.157). Sensitivity analysis reviewed several potential sources of heterogeneity between studies, such as average SUA level, study location, and outcome indicators.

Rheumatology

Cardiac & Cardiovascular Systems

Uric acid

cardiovascular diseases

meta-analysis

Cardiovascular Diseases (CVD) is one of the most challenging public health problems, which is the pathological condition of the heart, the blood vessel, or the pericardium. Cardiovascular diseases account for the majority of non-communicable disease deaths, 17.9 million per year, followed by cancer (8.8 million), respiratory diseases (3.8 million) and diabetes (1.5 million)[1] .Coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), and hypertension are the most important causes of mortality and morbidity in modern society[2]. Many risk factors affect CVD incidence. Traditional risk factor include behaviour-related risk factors such as smoking, obesity, alcoholism, unhealthy diet, lack of physical activity, depression, and physiological risk factors such as age, high blood pressure, dyslipidemia, and insulin resistance. Non-traditional risk factors include ankle-brachial index (ABI), high-sensitivity C-reactive protein (hsCRP) level, and coronary artery calcium (CAC) score. In the past, uric acid was regarded as an effective endogenous antioxidant produced by purine metabolism. It can chelate metal ions and react with effective bio-oxidants to produce relatively stable products. Its level is very important in humans due to the silencing of the uricase gene[3]. however, in recent decades, hyperuricemia has been considered as an independent risk factor of cardiovascular disease. The causes of this relationship is not yet clear, but possible mechanisms include NO reduction, oxidative stress, inflammatory processes, arterial stiffness, and endothelial damage. In addition, UA has been associated with smooth muscle cell proliferation, arterial calcification, platelet aggregation and thrombotic enhancement[4]. More importantly, the metabolism of purine from hypoxanthine to uric acid will produce a large amount of superoxides. And oxidative stress plays an important role in the pathophysiology of cardiovascular diseases [5].Since UA retains dual antioxidant and pro-oxidant capacity, the relationship between serum uric acid level and CVD is still contradictory. Several previous meta-analysis showed that hyperuricemia was significantly associated with the occurrence and mortality of stroke[6, 7], coronary heart disease[8–13], hypertension[14–16], atrial fibrillation[17] and acute coronary syndrome[18]. But none of these meta-analysis provided an overview of all major cardiovascular diseases. It would be interesting to determine whether the hyperuricemia-cardiovascular diseases association differed by CVD subtype. Besides, some of limiations may existed in the former studies, such as the possibility of publication bias, high heterogeneity, lack of individual participant data, and limited sample size in subgroup analysis. As a result, a meta analysis that contains more high-quality clinical trials requires longer follow up periods. Therefore, we performed an updated meta-analysis to obtain a more accurate estimate of the association between hyperuricemia and CVD.

We studied literature from the past 44 years through MEDLINE, EMBASE, and Cochrane library searches. We searched all these databases from creation to June 19, 2020. We studied all articles issued since January 1975 to evaluate the objective association between serum uric acid (SUA) and cardiovascular disease. Keywords including ["uric acid", OR "urate", OR "gout", OR "purine metabolism", OR "hyperuricemia"] AND ["cardiovascular disease", OR "myocardial infarction", OR "coronary artery disease", OR "heart failure", OR "fibrillation atrial", OR "hypertension"] And ["mortality", OR "risk factor", OR "hazard ratio", OR "odd ratio"] in various combinations. The following search strategy was used for MEDLINE: ((((((uric acid) OR (urate)) OR (gout)) OR (purine metabolism)) OR (hyperuricemia)) AND (( ((((cardiovascular disease) OR (myocardial infarction)) OR (coronary artery disease)) OR (heart failure)) OR (fibrillation atrial)) OR (hypertension))) AND (((((mortality) OR (risk factor)) OR (hazard ratio)) OR (odd ratio)). Then we limited the article types to clinical trails and randomized controlled trails. We used similar search strategies for the Cochrane library and EMBASE. Studies in the researches were identified thoroughly to ensure that relevant studies were not cross-referenced. We adopted the meta-analysis of observational studies in epidemiology (MOOSE) study guidelines[19]. We also searched the American College of Rheumatology Annual (ACR) and the European Association of Anti-Rheumatology Annual (EULAR) meeting reports and review articles for additional references.

We only considered studies that meet our inclusion criteria:1) prospective cohort and cross-sectional studies involving adult patients; 2) the follow-up period is longer than 3 months; 3) describe the relationship between hyperuricemia and the incidence of CVD; 4)the sample size exceeds 100 subjects; 5) reported adjusted risk estimates for the incidence of CVD, such as relative risk (RR), odds ratio (OR) or hazard ratio (HR) with a 95% confidence interval (95% CI); and, 6) researches on intervention and secondary prevention trials were excluded. No geographic restriction are implemented. Only English articles were selected.

Data extraction and quality assessment were conducted by two confirmed reviewers. Duplicate reports and review articles were checked to identify other sources of data. Full texts can be obtained if a study meets all the inclusion criteria or there is uncertainty about inclusion. The studies we selected are mainly randomized controlled clinical trials and a prospective cohort study concentrated on the relationship between SUA and various cardiovascular prognosis, including myocardial infarction (MI), coronary heart disease, all adverse cardiovascular events, cardiovascular mortality, and all-cause mortality. The definition of CVD was based on ICD-10 and included all CVDs that could be verified in the Life Lines database; MI, heart failure, atrial fibrillation, heart valve disorders, arrhythmia, aneurysm, stroke, thrombosis, atherosclerosis, narrowing carotid arteries and a history of coronary artery bypass grafting (CABG)[20] .Hyperuricemia was defined as serum uric acid levels of 7.0 mg/dl for men and 6.0 mg/dl for women[21].

The methodological quality assessment criteria was inspired by the Newcastle–Ottawa scale[22]. The quality score was calculated based on three main components of the cohort study: study groups selection (0–4 points), study groups comparability (0–2 points), and ascertaining of the results of interest (0–3 points). The higher the score, the better the methodological quality. We chose a random-effects model to calculate the combined hazard ratios (HR) or odds ratio (OR) of mortality from any CVD or CHD event. The data are expressed as relative risk (RR) with 95% CI[23]. I² was utilized to calculate the heterogeneity between studies (mild, 0-30%; moderate, 31-50%; high, >50%)[24].

Statistical analysis was performed using STATA SE 15. The metan, metabias, and metatrim commands[25] were used for all statistical analyses. A two-sided P-value below 0.05 is considered statistically significant. A subgroup analysis was performed to explore the heterogeneity of the study[26]. Statistical testing of the meta-regression analysis[27], Tau-squared and P-value can explore the potential sources of heterogeneity between studies. Forest plots and pooled estimates were registered[28]. Publication bias was assessed by using funnel plots[29]. Begg's rank correlation test and Egger's linear regression test were utilized to detect any asymmetry in the funnel chart that may be caused by publication bias[30]. We also performed a "trim and fill" method [31] to correct funnel plot asymmetry by simulating the hypothetical "missing" studies possibly caused by publication bias. Agreement between the before and after "trim and fill" RR provides confidence that the results are robust to possible publication bias. Finally, we calculated the fail-safe number[32], the number of non-significant studies that would find the P-value to non-significant, using 0.05 as the setting criterion.

Selection and characteristics of studies

The pertinent articles obtained from the literature search and the reference lists were introduced into software EndnoteX9 for management. A total of 3356 publications were identified (Figure 1). After removing duplicates we excluded 1710 citations, based on the screening of titles and abstracts we excluded 1479 citations, and after a thorough assessment of the full-text, we excluded 58 citations. Among 109 eligible full text articles, 87 papers were excluded because of: – duplicate results from the same group of authors were reported (n =4 ); a retrospective study was carried out (n =7); – with less than 3 months of follow-up (n=8 );– an outcome different from the one of interest was considered (n = 33); – with a sample size of fewer than 100 subjects (n=14 ); – definition of urate cut-offs or range limits were lacking (n =16 ); and – did not report adjusted risk estimates for CVD incidence(n =5). Finally, we identified 22 studies [33–54] that met our inclusion criteria. The methodological quality of all the included studies was moderate to high, with NOS score varying from 7 to 8 and a median of 8. Table 1 display the characteristics and effect sizes of the 22 studies of uric acid level and risk of CVD incidence. 22 prospective studies with a total number of 421802 participants were entered in the meta-analysis. Six studied health check-ups and outpatients[34, 35, 42, 43, 50], and the other sixteen studies were restricted to patients with cardiac vascular disease (CVD) [33, 36–41, 44, 45, 47–49, 51–54]. The datum was removed from 18 cohort studies and 4 cross-section studies[39, 48–50]. And no disagreements between the two reviewers regarding study inclusion. Of the twenty-two trials, three were conducted primarily in the United States[33, 41, 44]. Twelve studies were made in Asian countries [34, 38, 39, 42, 43, 46–52] and seven studies were from European countries [35–37, 40, 45, 53, 54]. The number of participants ranged from 324 in a study by Palazzuoli et al[51] to 197144 in the cohort study by Shin Kawasoe et al [40]. Twenty-one studies included both men and women. One study included only men[33], and none of the studies included only women. Nine studies utilized a lower cut-off value to define hyperuricemia for women opposed to men [34, 36, 38, 40, 42, 43, 51, 52, 54]. Five studies reported gender-specific outcome for CHD mortality[33, 34, 36, 43, 51]. Thirteen studies provided hazard ratio estimates[24, 35, 37, 38, 40–42, 44, 45, 47, 52–54], eight studies provided odds ratio[36, 39, 43, 46, 48–51] and one provided relative risk ratio[33].

Hyperuricemia and cardiac event incidence

The relationship between uric acid and cardiac event incidence could be presented in three ways: all-cause mortality (four studies, 11288 patients), mortality from CVDs (ten studies, 34897 patients), and other CVD incidences (eight studies, 375617 patients). All studies provided categorical data and none provided continuous data. Meta-analysis with a random-effects model suggested that there was problems with heterogeneity between hyperuricemia and cardiac event incidence (RR=1.30; 95% CI:1.24-1.37; I2=69.5%, P=0.00,Tau²=0.0062) (Figure 2-1). In mortality from CVDs, the pooled result suggested a consistent relationship between hyperuricemia and CVD mortality (RR=1.37; 95% CI:1.29-1.45; I2=31.4%, P=0.157) (Figure 2-2).

Sensitivity Analysis

Subgroup analysis

A subgroup analysis of cardiac event incidence needed to be done to investigate the heterogeneity of the studies. The selected subsets including the year of publication (Year before 2010, Year after 2010), sample size (<1000, 1000-10000, 10001-100000, >100000), epidemiological research methods (cohort study, cross-sectional study), risk indicator (HR, OR, RR), study location (USA, Asian, Europe), sample source (patient had CVD versus Healthy population), CVD categories(coronary artery disease,hypertension,heart failure,atrial fibrillation,stroke) ,and outcome indicators (all-cause mortality, cardiovascular disease mortality, other cardiovascular morbidities). As the odds ratios could be deemed to be accurate estimates of risk ratios, we, therefore, utilized RR as the common measure of association across studies. Published year before 2010(RR=1.32, 95% CI: 1.23-1.41; I2=24.8%, P=0.22), sample size less than one thousand(RR=1.24,95%CI: 1.12-1.37; I2=0.0%, P=0.56), lived in Europe (RR=1.39, 95% CI: 1.31-1.48, I2=41.7%, P=0.11), coronary artery disease(RR=1.27, 95% CI: 1.19-1.37, I2=42.2%, P=0.11), hypertension (RR=1.26, 95% CI: 1.19-1.33, I2=30.7%, P=0.22) and cardiovascular disease mortality (RR=1.37, 95% CI: 1.29-1.45, I2=31.4%, P=0.16) subgroups showed that hyperuricemia was associated with an increased risk of cardiac event incidence (table 2).

Meta-regression

Meta-regression analysis showed that mean SUA level (OR=1.04, p=0.047, Adj R²=23.95%,tau² = 0.0053, in the univariate models) and CVD prevalence at baseline (OR=1.005, p=0.09, Adj R²=24.2%,tau² = 0.005, in the univariate models)

may effect the heterogeneity in this study for analyses of cardiac event incidence. Similarly, meta-regression analysis also showed that Study location and outcome indicators (p=0.002, I²=27.3%,Adj R²=83.72%,tau² = 0.0011, in the multivariate models)may contribute to the heterogeneity in this study(table 3).

Publication Bias Assessment

Funnel plots for CVD incidence were visually examined (Figure 3-1). No evidence of publication bias for studies was noted in the funnel plot, Egger regression asymmetry test (p=0.15), or Begg's test (p=0.26). Then we undertook a sensitivity analysis using public trim and fill method (Figure 3-2), which conservatively imputes hypothetical negative unpublished studies to mirror the progressive studies that cause funnel plot asymmetry. Four imputed studies for categorical data were needed to produce produce symmetrical funnel plots. The fail-safe number was 3384, indicating that 3384 ''negative'' studies would be necessary to increase the P-value for the meta-analysis of above 0.05.

Although there are several reports[55, 56–59] of an association between high SUA and poor clinical outcomes in cardiovascular disease such as coronary heart disease, hypertension, acute myocardial infarction, heart failure, and atrial fibrillation, it has been unclear whether UA is a marker of adverse prognosis or a risk factor for CVD[60]. Some researches showed that uric acid appeared to be an antioxidant [61, 62]. It may function as one of the major antioxidants in plasma. Those research[63] led quite a few of the authorities to consider uric acid not to be a true cardiovascular risk factor. However, there is necessary some studies[64] found that elevated uric acid often preceded by hypertension. Those studies were also consistent with the possibility that uric acid might have a causal role in cardiac vascular disease development[55, 65]. As a consequence, the central paradox[66] is to determine whether uric acid acts as an antioxidant or pro-oxidant. Sautin YY et al [67] hypotheses that when uric acid is inside a cell, it activates NADPH oxidase and generates oxidants, whereas, in the extracellular environment, it functions as an antioxidant. Although uric acid is only an antioxidant in the extracellular environment, they demonstrated that it is pro-oxidative inside the cell.

Our meta-analysis suggested that hyperuricemia was associated with an increased risk of cardiac mortality, which has a moderate elevation of relative risk factor (RR=1.37, 95% CI 1.29-1.45; I2=31.4%, P=0.157). This outcome is not dependent on traditional CVD risk factors, such as age, gender, older age, ethnicity, and location. In subgroup analyses and meta-regression, hyperuricemia appeared to greatly increase the risk of CHD incidence in people lived in Europe, patients who had higher serum uric acid level.

Our study has some limitations. Several potential limitations of this study are specific meta-analyses. First of all, there were different definitions of hyperuricemia across the studies which influence the subgroup[68]. However, different cut-off levels for hyperuricemia did not modify the study estimates of CHD risk derived from our meta-regression analysis. Secondly, in the sensitivity analysis such as subgroup analysis and meta regression, some groups has limited number of included studies which make us could not get a precise conclusions. Thirdly, this observational study may have some unmeasured confounding risk factors, such as medications intake and some other complications. Our study also presents several notable strengths. We selected large prospective studies which at least 100 subjects with follow-up periods of more than 6 months, which helped increase the precision of estimates while minimizing heterogeneity. Unmeasured confounding is likewise a widespread problem in observational studies, including prospective cohorts. So we performed outcome-specific subgroup analyses of the studies fully adjusting for traditional CVD risk factors. Multivariate meta-regression analysis further examined numerous potential sources of heterogeneity between the studies such as sample size, ethnicity, location, patients who had a history of CVD, higher serum uric acid level. It also indicates that as a risk factor, hyperuricemia is closer related to CVD death than other CVD incidence. Prospective clinical studies are needed to investigate whether a reduction in uric acid levels could prevent CVD or metabolic syndrome.

In conclusion, there is a modestly increased risk for CVD mortality associated with hyperuricemia in our meta-analysis. More pronounced increased risk factors for CVD mortality should be subject to future research. It would mean a lot to design further large, long-term studies that determine the effect of urate-lowering therapy on cardiovascular disease. Further studies are required to better establish uric acid's potential causative role in CVD.

Competing interest:

The authors declare that they have no competing interests

Mortality, G. B. D. & Causes of Death, C. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015., 388 (10053), 1459–544 (2016).
Mozaffarian, D. et al. Heart disease and stroke statistics--2015 update: a report from the American Heart Association., 131 (4), e29–322 https://doi.org/doi: 10.1161 (2015).
Watanabe, S. et al. Uric acid, hominoid evolution, and the pathogenesis of salt-sensitivity., 40 (3), 355–60 https://doi.org/doi: 10.1161/01. (2002).
Feig, D. I., Kang, D. H. & Johnson, R. J. Uric acid and cardiovascular risk. N Engl J Med, 359 (17), 1811–21 https://doi.org/doi: 10.1056 (2008).
Suvorava, T. & Kojda, G. Reactive oxygen species as cardiovascular mediators: lessons from endothelial-specific protein over expression mouse models. Biochim Biophys Acta, 1787 (7), 802–10 (2009).
Li, M. et al. Hyperuricemia and risk of stroke: a systematic review and meta-analysis of prospective studies. Atherosclerosis, 232 (2), 265–70 (2014).
Kim, S. Y. et al. Hyperuricemia and risk of stroke: a systematic review and meta-analysis. Arthritis and rheumatism, 61 (7), 885–92 (2009).
Liang, L. et al. The association between hyperuricemia and coronary artery calcification development: A systematic review and meta-analysis. Clin Cardiol, 42 (11), 1079–86 (2019).
He, C. et al. Prognostic value of hyperuricemia in patients with acute coronary syndrome: A meta-analysis. Eur J Clin Invest, 49 (4), e13074 (2019).
Zuo, T. et al. Hyperuricemia and coronary heart disease mortality: a meta-analysis of prospective cohort studies. BMC Cardiovasc Disord, 16 (1), 207 (2016).
Li, M. et al. Hyperuricemia and the risk for coronary heart disease morbidity and mortality a systematic review and dose-response meta-analysis. Sci Rep, 6, 19520 (2016).
Braga, F. et al. Hyperuricemia as risk factor for coronary heart disease incidence and mortality in the general population: a systematic review and meta-analysis. Clin Chem Lab Med, 54 (1), 7–15 (2016).
Kim, S. Y. et al. Hyperuricemia and coronary heart disease: a systematic review and meta-analysis. Arthritis Care Res (Hoboken), 62 (2), 170–80 (2010).
Qin, T. et al. Hyperuricemia and the Prognosis of Hypertensive Patients: A Systematic Review and Meta-Analysis. J Clin Hypertens (Greenwich), 18 (12), 1268–78 (2016).
Wang, J. et al. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PloS one, 9 (12), e114259 (2014).
Grayson, P. C. et al. Hyperuricemia and incident hypertension: a systematic review and meta-analysis. Arthritis Care Res (Hoboken), 63 (1), 102–10 (2011).
Xu, X. et al. Hyperuricemia is independently associated with increased risk of atrial fibrillation: A meta-analysis of cohort studies. International journal of cardiology, 184, 699–702 (2015).
He, C. et al. Prognostic value of hyperuricemia in patients with acute coronary syndrome: A meta-analysis. Eur J Clin Invest, 49 (4), e13074 (2019).
Stroup, D. F. et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA, 283 (15), 2008–12 (2000).
DeVault, K. ICD-10-PCS From the Heart: Cardiovascular Procedures. J AHIMA, 86 (9), 62–4 (2015).
Epidemiology (MOOSE) group.JAMA2000; 283(15):2008–12 doi: 10.1001
Wilcox, W. R., Khalaf, A., Weinberger, A., Kippen, I. & Klinenberg, J. R. Solubility of uric acid and monosodium urate. Med Biol Eng, 10 (4), 522–31 https://doi.org/doi: 10.1007/ (1972).
Wells, G. S. et al. M.,. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Wells, G., editor Ottawa
DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control Clin Trials, 7 (3), 177–88 https://doi.org/doi: 10.1016 (1986).
Higgins, J. P. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat Med, 21 (11), 1539–58 https://doi.org/doi: 10.1002 (2002).
Bagos, P. G. Meta-analysis in Stata using gllamm. Res. synth. methods, 6 (4), 310–32 (2015).
Kasenda, B. et al. Subgroup analyses in randomised controlled trials: cohort study on trial protocols and journal publications. BMJ, 349, g4539 https://doi.org/doi: 10.1136. (2014).
Huang, J. Q., Zheng, G. F., Irvine, E. J. & Karlberg, J. Assessing heterogeneity in meta-analyses of Helicobacter pylori infection-related clinical studies: a critical appraisal. Chin J Dig Dis, 5 (3), 126–33 https://doi.org/doi: 10.1111 (2004).
Verhagen, A. P., Ferreira, M. L., Davey Smith, G., Schneider, M. & Minder, C. Forest plots. J Physiother 2014;60(3):170-3 doi: 10.1016 Egger M,, Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315(7109):629-34 doi: 10.1136
Sterne, J. A. & Egger, M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol, 54 (10), 1046–55 https://doi.org/doi: 10.1016. (2001).
Duval, S. & Tweedie, R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis., 56 (2), 455–63 https://doi.org/doi: 10.1111 (2000).
Fragkos, K. C., Tsagris, M. & Frangos, C. C. Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number. Int Sch Res Notices, 2014, 825383 https://doi.org/doi: 10.1155 (2014).
GROUP TCDPR. Serum uric acid: its association with other risk factors and with mortality in coronary heart disease. Journal of chronic diseases, 29 (9), 557–69 (1976).
Wang, J. G. et al. Prognostic significance of serum creatinine and uric acid in older Chinese patients with isolated systolic hypertension., 37 (4), 1069–74 (2001).
De Leeuw, P. W. et al. Prognostic significance of renal function in elderly patients with isolated systolic hypertension: Results from the Syst-Eur trial. Journal of the American Society of Nephrology, 13 (9), 2213–22 https://doi.org/doi: 10.1097 (2002).
Wheeler, J. G., Juzwishin, K. D. M., Eiriksdottir, G., Gudnason, V. & Danesh, J. Serum uric acid and coronary heart disease in 9,458 incident cases and 155,084 controls: Prospective study and meta-analysis. PLoS Medicine, 2 (3), 0236–43 https://doi.org/doi: 10.1371 (2005).
Hjortnaes, J. et al. Serum uric acid levels and risk for vascular diseases in patients with metabolic syndrome. J Rheumatol, 34 (9), 1882–7 (2007). 07/13/0811 |.
Brodov, Y., Behar, S., Boyko, V. & Chouraqui, P. Effect of the metabolic syndrome and hyperuricemia on outcome in patients with coronary artery disease (from the Bezafibrate Infarction Prevention Study). American journal of cardiology, 106 (12), 1717–20 https://doi.org/10.1016| (2010).
Lim, H. E. et al. Clinical value of serum uric acid in patients with suspected coronary artery disease. Korean Journal of Internal Medicine, 25 (1), 21–26 https://doi.org/10.3904| (2010).
Wiik, B. P. et al. Serum uric acid is associated with new-onset diabetes in hypertensive patients with left ventricular hypertrophy: the LIFE Study. American journal of hypertension, 23 (8), 845–51 https://doi.org/10.1038| (2010).
Wu, A. H. et al. Uric acid level and allopurinol use as risk markers of mortality and morbidity in systolic heart failure. Am Heart J, 160 (5), 928–33 https://doi.org/10.1016| (2010).
Chao, T. F. et al. The association between hyperuricemia, left atrial size and new-onset atrial fibrillation. International journal of cardiology, 168 (4), 4027–32 https://doi.org/10.1016| (2013).
Song, K. et al. Does decreasing serum uric acid level prevent hypertension? - a nested RCT in cohort study: rationale, methods, and baseline characteristics of study cohort. BMC Public Health, 13, 1069 https://doi.org/10.1186| (2013).
Vaduganathan, M. et al. Relation of serum uric acid levels and outcomes among patients hospitalized for worsening heart failure with reduced ejection fraction (from the efficacy of vasopressin antagonism in heart failure outcome study with tolvaptan trial). American Journal of Cardiology, 114 (11), 1713–21 https://doi.org/10.1016| (2014).
Lazzeri, C., Valente, S., Chiostri, M. & Gensini, G. F. Long-term prognostic role of uric acid in patients with ST-elevation myocardial infarction and renal dysfunction. Journal of Cardiovascular Medicine, 16 (11), 790–94 https://doi.org/10.2459| (2015).
Li, Q. et al. The Association between Serum Uric Acid Levels and the Prevalence of Vulnerable Atherosclerotic Carotid Plaque: A Cross-sectional Study. Sci Rep, 5, 10003 (2015). 10.1038|.
Chen, L. H. et al. Sex-specific Association Between Uric Acid and Outcomes After Acute Ischemic Stroke: A Prospective Study from CATIS Trial. Sci Rep, 6, 38351 (2016). 10.1038|.
Cho, J., Kim, C., Kang, D. R. & Park, J. B. Hyperuricemia and uncontrolled hypertension in treated hypertensive patients: K-MetS Study. Medicine (United States), 95 (28), https://doi.org/doi: 10.1097|. (2016).
Huang, W. M. et al. Determinants and Prognostic Impact of Hyperuricemia in Hospitalization for Acute Heart Failure. Circ J, 80 (2), 404–10 https://doi.org/10.1253| (2016).
Kawasoe, S. et al. Uric Acid Level and Prevalence of Atrial Fibrillation in a Japanese General Population of 285,882. Circ J, 80 (12), 2453–59 https://doi.org/10.1253| (2016).
Palazzuoli, A., Ruocco, G., De Vivo, O., Nuti, R. & McCullough, P. A. Prevalence of Hyperuricemia in Patients With Acute Heart Failure With Either Reduced or Preserved Ejection Fraction. Am J Cardiol, 120 (7), 1146–50 https://doi.org/10.1016| (2017).
Shi, X. et al. Prospective study of serum uric acid levels and stroke in a Chinese hypertensive cohort. Clinical and Experimental Hypertension, 39 (6), 527–31 https://doi.org/10.1080| (2017).
Mantovani, A. et al. Prognostic impact of elevated serum uric acid levels on long-term outcomes in patients with chronic heart failure: a post-hoc analysis of the GISSI-HF (Gruppo Italiano per lo Studio della Sopravvivenza nella Insufficienza Cardiaca-Heart Failure) trial. Metabolism: clinical and experimental, 83, 205–15 https://doi.org/10.1016| (2018).
Mogensen, U. M. et al. Sacubitril/valsartan reduces serum uric acid concentration, an independent predictor of adverse outcomes in PARADIGM-HF. European journal of heart failure, 20 (3), 514–22 https://doi.org/10.1002| (2018).
Borghi, C. et al. Serum uric acid levels are associated with cardiovascular risk score: A post hoc analysis of the EURIKA study. International journal of cardiology, 253, 167–73 doi (2018). : 10.1016|.
Valaiyapathi, B., Siddiqui, M., Oparil, S., Calhoun, D. A. & Dudenbostel, T. High uric acid levels correlate with treatment-resistant hypertension. Hypertension 2017;70
Hyperuricemia is associated with short-term outcomes in elderly patients with acute myocardial infarction. Aging clinical and experimental research 2018:1-5 doi: 10.1007|
Tsutsui, H. Hyperuricemia in heart failure. Journal of Cardiac Failure, 22 (9), S157 (2016).
Yang, P. S. et al. Serum uric acid concentration and risk of atrial fibrillation: A Mendelian randomization analysis in Asian population. Europace, 20, i229 https://doi.org/10.1093| (2018).
Martinez-Quintana, E., Tugores, A. & Rodriguez-Gonzalez, F. Serum uric acid levels and cardiovascular disease: the Gordian knot. Journal of thoracic disease, 8 (11), E1462–E66 https://doi.org/10.21037| (2016).
Llull, L. et al. Uric Acid Therapy Improves Clinical Outcome in Women With Acute Ischemic Stroke., 46 (8), 2162–7 https://doi.org/10.1161| (2015).
Amaro, S. et al. Uric acid improves glucose-driven oxidative stress in human ischemic stroke. Ann Neurol, 77 (5), 775–83 doi (2015). : 10.1002]|.
Neogi, T., Terkeltaub, R., Ellison, R. C., Hunt, S. & Zhang, Y. Serum urate is not associated with coronary artery calcification: The NHLBI Family Heart Study. Journal of Rheumatology, 38 (1), 111–17 https://doi.org/10.3899| (2011).
Cicero, A. F. G. et al. High serum uric acid is associated to poorly controlled blood pressure and higher arterial stiffness in hypertensive subjects. European Journal of Internal Medicine, 37, 38–42 https://doi.org/10.1016| (2017).
Calvo Aranda, E. et al. Cardiovascular risk and endothelial dysfunction in patients with gout: Experience in a Multihospital Spanish group. Annals of the Rheumatic Diseases, 75, 375 (2016). 10.1136|.
Shah, A. & Keenan, R. T. Gout, hyperuricemia, and the risk of cardiovascular disease: Cause and effect? Curr. Rheumatol. Rep, 12 (2), 118–24 https://doi.org/10.1007| (2010).
Sautin, Y. Y. & Johnson, R. J. Uric acid: the oxidant-antioxidant paradox. Nucleosides Nucleotides Nucleic Acids, 27 (6), 608–19 doi (2008). : 10.1080|.
Ruoff, G. & Edwards, N. L. Overview of Serum Uric Acid Treatment Targets in Gout: Why Less Than 6 mg/dL? Postgrad Med, 128 (7), 706–15 doi (2016). : 10.1080|.

Due to technical limitations, table 1 to 3 is only available as a download in the Supplemental Files section.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Meta-analysis for the Relationship Between Hyperuricemia and CVD Incidence

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1