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. 2010 Jun;210(2):474-8.
doi: 10.1016/j.atherosclerosis.2009.12.013. Epub 2009 Dec 16.

Sex and age interaction with genetic association of atherogenic uric acid concentrations

Anita Brandstätter  1 , Claudia Lamina, Stefan Kiechl, Steven C Hunt, Stefan Coassin, Bernhard Paulweber, Felix Kramer, Monika Summerer, Johann Willeit, Lyudmyla Kedenko, Ted D Adams, Florian Kronenberg
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Sex and age interaction with genetic association of atherogenic uric acid concentrations

Anita Brandstätter et al. Atherosclerosis. 2010 Jun.

"V体育官网" Abstract

Background: High serum uric acid levels are associated with gout, atherosclerosis and cardiovascular disease VSports手机版. Three genes (SLC2A9, ABCG2, and SLC17A3) were reported to be involved in the regulation of uric acid levels. .

Design and methods: SNPs rs2231142 (ABCG2) and rs1165205 (SLC17A3) were genotyped in three cohorts (n=4492) and combined with previously genotyped SNPs within SLC2A9 (rs6855911, rs7442295, rs6449213, rs12510549) V体育安卓版. .

Results: Each copy of the minor allele decreased uric acid levels by 0. 30-0. 38 mg/dL for SLC2A9 (p values: 10(-20)-10(-36)) and increased levels by 0. 34 mg/dL for ABCG2 (p=1. 1x10(-16)). SLC17A3 influenced uric acid levels only modestly. Together the SNPs showed graded associations with uric acid levels of 0. 111 mg/dL per risk allele (p=3. 8x10(-42)). In addition, we observed a sex-specific interaction of age with the association of SLC2A9 SNPs with uric acid levels, where increasing age strengthened the association of SNPs in women and decreased the association in men V体育ios版. .

Conclusions: Genetic variants within SLC2A9,ABCG2 and SLC17A3 show highly significant associations with uric acid levels, and for SNPs within SLC2A9 this association is strongly modified by age and sex. VSports最新版本.

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Figures

Fig. 1
Fig. 1
(A) The frequency of copy number variations (CNVs) in 270 HapMap samples in the region of the analyzed SNPs. (B) Sex-specific effects of the analyzed SNPs on uric acid concentrations. The bars show the difference in uric acid levels per minor allele compared to the wild-type stratified for men and women. The p values above the graphs are derived from the linear mixed effect model of the combined analysis of the additive model using standardized uric acid levels (adjusted for age and BMI). The p values below the graph are for the SNP*sex interaction terms. (C) Sex-specific effects of an additive genetic risk score of the number of risk alleles within ABCG2, SLC2A9 and SLC17A3 that are associated with increased uric acid levels. The dots indicate mean values (±standard errors) of uric acid levels as indicated on the left y-axis; the histogram represents the distribution of number of subjects in each group of risk alleles as indicated in the right y-axis. The risk alleles for this score were allele A for rs1165205, rs2231142, rs6855911, and rs7442295 as well as allele T for rs12510549 and rs6449213. The range of the score was from 0 to 12.
Fig. 2
Fig. 2
Interaction of age and gender on the genetic association of SLC2A9 SNPs with uric acid levels. The x-axis corresponds to the age in quartiles; the y-axis represents the ß-estimates +/−95% confidence intervals (CI) of the additive effect of the minor allele of each SNP. The p value of the SNP*age*sex interaction term is indicated on the upper left corner of each graph.
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"VSports手机版" References

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    1. Döring A, Gieger C, Mehta D, et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat Genet. 2008;40:430–436. - V体育官网 - PubMed

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