Baseline serum uric acid level as a predictor of cardiovascular disease related mortality and all-cause mortality: A meta-analysis of prospective studies

Zhao, Gang; Huang, Lan; Song, Mingbao; Song, Yaoming

doi:10.1016/j.atherosclerosis.2013.08.023

Cited by 243 publications

(186 citation statements)

References 42 publications

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“…-Zhao et al [13] No dose-response investigation was done in that work to support some causal relationships between uricemia and CV events, in which that analysis is one of the most important among Hill's criteria [6]. -Li and colleagues [14] concluded that hyperuricemia increases the risk of CHD events (RR = 1.13 [1.05-1.21]).…”

Section: What Results Do Other Meta-analysis Show?mentioning

confidence: 99%

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Hyperuricemia does not seem to be an independent risk factor for coronary heart disease

Battaggia

Scalisi²,

Puccetti

2017

Clinical Chemistry and Laboratory Medicine (CCLM)

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Federica Braga and coworkers [1] stated recently that hyperuricemia shows to be an independent predictor of coronary heart disease (CHD) risk (RR CHD = 1.206 [1.066-1.364]; RR CHD death = 1.209 [1.003-1.457]), mainly for the results found in women. That message seems to be of great importance: if it is true, the treatment of hyperuricemia should be included in the landscape of the therapeutic strategies to reduce the coronary risk. To ascertain the methodologic validity and robustness of that work, we considered it as a critical appraisal of some methodological aspects.In our approach, (a) we evaluated the overall quality of that systematic review through the AMSTAR checklist [2]; (b) we recovered the nine studies selected by Braga and colleagues [1] in order to repeat the meta-analysis and quantified the heterogeneity through I 2 statistic [3]; (c) we launched new subgroup and sensitivity and metaregression analyses in order to better explore the heterogeneity, considering the following as potential effect modifiers: gender, number of CHD covariates used in the adjustment models (i.e. age, BMI, total cholesterol or LDL-cholesterol or presence of dyslipidemia, blood pressure values or presence of hypertension, smoke, glucose values or presence of diabetes) and lack of nutritional information; (d) we estimated the prevalence of metabolic syndrome in single-trial samples using an Italian epidemiological research as reference sample [4]; (e) we investigated the quality of included trials and their risk of bias through the ACROBAT Cochrane checklist [5]; and (f) we used a generalized least-squares regression model to inspect some dose-response effect [6] both at trial level and with a doseresponse meta-analysis; the goodness of fit was explored with a χ 2 -test [7]. We restricted all our described analyses to the end point 'CHD incidence'; their methodological details are available in the online Supplementary material. What were our results?1. The quality of the meta-analysis assessed with the AMSTAR checklist [2] appears to be medium/low: only 4/11 items were fully satisfied, 3/11 not satisfied and 4/11 uncertain. 2. The metaregression (Figure 1) demonstrates that the number of confounders could be an important cause of heterogeneity, with the risk ratio of CHD associated to hyperuricemia decreasing by 13% for each covariate added to the model (p = 0.056). The subgroup analysis using the number of covariates as effect modifier coherently indicates that the role of hyperuricemia tends to disappear in the best adjusted models (test of interaction, p = 0.056). Notably, the trials that were not adjusted for nutritional status [8][9][10]

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Section: What Results Do Other Meta-analysis Show?mentioning

confidence: 99%

“…Three other meta-analyses explored the relation between uricemia and fatal or non-fatal CHD end points in the last decade [12][13][14].…”

Section: What Results Do Other Meta-analysis Show?mentioning

confidence: 99%

Hyperuricemia does not seem to be an independent risk factor for coronary heart disease

Battaggia

Scalisi²,

Puccetti

2017

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…The score was defined as the sum of the number of UA-increasing alleles at each locus multiplied by the respective b coefficient reported from the meta-analysis. 16 Histograms of the distribution of the GRS as well as scatter plots showing the correlation between GRS and uric acid are shown in the supplemental Figures 1 and 2 210 for GRS 28 , GRS 14 , and GRS 8 , respectively (Supplemental Table 1). In all further analyses, we selected GRS 8 , which showed strong association with UA although it contained no SNPs with suspected pleiotropy in the LURIC study.…”

Section: Calculation Of Weighted Grssmentioning

confidence: 99%

“…We calculated weighted GRSs for UA concentration based on all 28 SNPs (GRS 28 ) and all 14 SNPs with no apparent association to other major risk factors (GRS 14 ). Eight of the 14 SNPs that did not reveal pleiotropic effects showed an effect directional in accordance with the published meta-analysis and only these were used to calculate the final genetic risk score (GRS 8 ).…”

Section: Calculation Of Weighted Grssmentioning

confidence: 99%

Uric Acid and Cardiovascular Events

Kleber

Delgado

Grammer

et al. 2015

Journal of the American Society of Nephrology

246

156

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Obesity and diets rich in uric acid-raising components appear to account for the increased prevalence of hyperuricemia in Westernized populations. Prevalence rates of hypertension, diabetes mellitus, CKD, and cardiovascular disease are also increasing. We used Mendelian randomization to examine whether uric acid is an independent and causal cardiovascular risk factor. Serum uric acid was measured in 3315 patients of the Ludwigshafen Risk and Cardiovascular Health Study. We calculated a weighted genetic risk score (GRS) for uric acid concentration based on eight uric acid-regulating single nucleotide polymorphisms. Causal odds ratios and causal hazard ratios (HRs) were calculated using a two-stage regression estimate with the GRS as the instrumental variable to examine associations with cardiometabolic phenotypes (cross-sectional) and mortality (prospectively) by logistic regression and Cox regression, respectively. Our GRS was not consistently associated with any biochemical marker except for uric acid, arguing against pleiotropy. Uric acid was associated with a range of prevalent diseases, including coronary artery disease. Uric acid and the GRS were both associated with cardiovascular death and sudden cardiac death. In a multivariate model adjusted for factors including medication, causal HRs corresponding to each 1-mg/dl increase in genetically predicted uric acid concentration were significant for cardiovascular death (HR, 1.77; 95% confidence interval, 1.12 to 2.81) and sudden cardiac death (HR, 2.41; 95% confidence interval, 1.16 to 5.00). These results suggest that high uric acid is causally related to adverse cardiovascular outcomes, especially sudden cardiac death.

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“…This is a particular problem for studies of allopurinol -the drug is given only for gout, and gout is closely associated with elevated urate levels. Elevated urate is in turn a powerful marker of vascular disease and death [26], and vascular disease is in turn associated both with osteoporosis and with functional impairment [27]. In our analysis, allopurinol use was much more common in those with a previous hospital admission for myocardial infarction or heart failure.…”

Section: Discussionmentioning

confidence: 63%