Dataset Information

Evaluation of the diet wide contribution to serum urate levels: meta-analysis of population based cohorts.

ABSTRACT:

Objective

To systematically test dietary components for association with serum urate levels and to evaluate the relative contributions of estimates of diet pattern and inherited genetic variants to population variance in serum urate levels.

Design

Meta-analysis of cross sectional data from the United States.

Data sources

Five cohort studies.

Review methods

16?760 individuals of European ancestry (8414 men and 8346 women) from the US were included in analyses. Eligible individuals were aged over 18, without kidney disease or gout, and not taking urate lowering or diuretic drugs. All participants had serum urate measurements, dietary survey data, information on potential confounders (sex, age, body mass index, average daily calorie intake, years of education, exercise levels, smoking status, and menopausal status), and genome wide genotypes. The main outcome measures were average serum urate levels and variance in serum urate levels. ? values (95% confidence intervals) and Bonferroni corrected P values from multivariable linear regression analyses, along with regression partial R² values, were used to quantitate associations.

Results

Seven foods were associated with raised serum urate levels (beer, liquor, wine, potato, poultry, soft drinks, and meat (beef, pork, or lamb)) and eight foods were associated with reduced serum urate levels (eggs, peanuts, cold cereal, skim milk, cheese, brown bread, margarine, and non-citrus fruits) in the male, female, or full cohorts. Three diet scores, constructed on the basis of healthy diet guidelines, were inversely associated with serum urate levels and a fourth, data driven diet pattern positively associated with raised serum urate levels, but each explained ?0.3% of variance in serum urate. In comparison, 23.9% of variance in serum urate levels was explained by common, genome wide single nucleotide variation.

Conclusion

In contrast with genetic contributions, diet explains very little variation in serum urate levels in the general population.

SUBMITTER: Major TJ

PROVIDER: S-EPMC6174725 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of the diet wide contribution to serum urate levels: meta-analysis of population based cohorts.

Major Tanya J TJ Topless Ruth K RK Dalbeth Nicola N Merriman Tony R TR

BMJ (Clinical research ed.) 20181010

<h4>Objective</h4>To systematically test dietary components for association with serum urate levels and to evaluate the relative contributions of estimates of diet pattern and inherited genetic variants to population variance in serum urate levels.<h4>Design</h4>Meta-analysis of cross sectional data from the United States.<h4>Data sources</h4>Five cohort studies.<h4>Review methods</h4>16 760 individuals of European ancestry (8414 men and 8346 women) from the US were included in analyses. Eligibl ...[more]

PMID: 30305269

Similar Datasets

Project description:BackgroundPrevious studies have noted significant variation in serum urate (sUA) levels, and it is unknown how this influences the accuracy of hyperuricemia classification based on single data points. Despite this known variability, hyperuricemic patients are often used as a control group in gout studies. Our objective was to determine the accuracy of hyperuricemia classifications based on single data points versus multiple data points given the degree of variability observed with serial measurements of sUA.MethodsData was analyzed from a cross-over clinical trial of urate-lowering therapy in young adults without a gout diagnosis. In the control phase, sUA levels used for this analysis were collected at 2-4 week intervals. Mean coefficient of variation for sUA was determined, as were rates of conversion between normouricemia (sUA ≤6.8 mg/dL) and hyperuricemia (sUA > 6.8 mg/dL).ResultsMean study participant (n = 85) age was 27.8 ± 7.0 years, with 39% female participants and 41% African-American participants. Mean sUA coefficient of variation was 8.5% ± 4.9% (1 to 23%). There was no significant difference in variation between men and women, or between participants initially normouricemic and those who were initially hyperuricemic. Among those initially normouricemic (n = 72), 21% converted to hyperuricemia during at least one subsequent measurement. The subgroup with initial sUA < 6.0 (n = 54) was much less likely to have future values in the range of hyperuricemia compared to the group with screening sUA values between 6.0-6.8 (n = 18) (7% vs 39%, p = 0.0037). Of the participants initially hyperuricemic (n = 13), 46% were later normouricemic during at least one measurement.ConclusionSingle sUA measurements were unreliable in hyperuricemia classification due to spontaneous variation. Knowing this, if a single measurement must be used in classification, it is worth noting that those with an sUA of < 6.0 mg/dL were less likely to demonstrate future hyperuricemic measurements and this could be considered a safer threshold to rule out intermittent hyperuricemia based on a single measurement point.Trial registrationData from parent study ClinicalTrials.gov Identifier: NCT02038179 .

Project description:BACKGROUND:The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. METHODS AND FINDINGS:A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. CONCLUSION:In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

Dataset Information

Evaluation of the diet wide contribution to serum urate levels: meta-analysis of population based cohorts.

Objective

Design

Data sources

Review methods

Results

Conclusion

Publications

Evaluation of the diet wide contribution to serum urate levels: meta-analysis of population based cohorts.

Similar Datasets

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