title: Association between serum uric acid and the metabolic syndrome among a middle- and old-age Chinese population.
abstract: Our aim was to study whether there is causal association between serum uric acid and metabolic syndrome (MetS). A cross-sectional study was performed, including a total of 27,009 subjects (23,345 subjects having uric acid data) from the Dongfeng-Tongji Cohort study. The MetS was defined by the International Diabetes Foundation criteria of 2005. Association analysis was performed by logistic regression. A genetic risk score was calculated by adding the uric acid increasing alleles in two SNPs (rs11722228 in SLC2A9 and rs2231142 in ABCG2) which were identified from our genome-wide association study on uric acid levels. The causal association was examined by mendelian randomization analysis. Among a middle- and old-age Chinese population, serum uric acid concentrations were strongly associated with the risk of MetS and its several components (P < 0.0001). The effects were stronger in women than in men. Despite the lack of statistical significance, both SNPs exhibited a trend with increased MetS risk (rs11722228, OR = 1.06, 95 % CI 0.99-1.14; rs2231142, OR = 1.02, 95 % CI 0.95-1.10), consistent with their increasing uric acid effects. Each additional uric acid increasing allele in the genetic risk score was associated with 3 % increased MetS risk (OR = 1.03, 95 % CI 0.98-1.09; P = 0.23). Further adjustment for serum uric acid attenuated the trend of individual SNP and genetic risk score with increased MetS risk (all OR < 1.0). These findings suggested that serum uric acid was associated with MetS risk in a middle- and old-age Chinese population. Whether this association was causal remained to be investigated in the future studies.
authors.affiliation: Institute of Occupational Medicine and the Ministry of Education (MOE) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China.
authors.forename: Xiayun
authors.initials: X
authors.lastname: Dai
authors.forename: Jing
authors.initials: J
authors.lastname: Yuan
authors.forename: Ping
authors.initials: P
authors.lastname: Yao
authors.forename: Binyao
authors.initials: B
authors.lastname: Yang
authors.forename: Lixuan
authors.initials: L
authors.lastname: Gui
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Zhang
authors.forename: Huan
authors.initials: H
authors.lastname: Guo
authors.forename: Youjie
authors.initials: Y
authors.lastname: Wang
authors.forename: Weihong
authors.initials: W
authors.lastname: Chen
authors.forename: Sheng
authors.initials: S
authors.lastname: Wei
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Miao
authors.forename: Xiulou
authors.initials: X
authors.lastname: Li
authors.forename: Xinwen
authors.initials: X
authors.lastname: Min
authors.forename: Handong
authors.initials: H
authors.lastname: Yang
authors.forename: Weimin
authors.initials: W
authors.lastname: Fang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Liang
authors.forename: Frank B
authors.initials: FB
authors.lastname: Hu
authors.forename: Tangchun
authors.initials: T
authors.lastname: Wu
authors.forename: Meian
authors.initials: M
authors.lastname: He
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000483:Alleles; D044466:Asian People / Q000235:genetics*; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008297:Male; D057182:Mendelian Randomization Analysis; D024821:Metabolic Syndrome / Q000097:blood* / Q000453:epidemiology / Q000235:genetics; D008875:Middle Aged; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide; D015995:Prevalence; D012307:Risk Factors; D014527:Uric Acid / Q000097:blood*
chemical_list: D014527:Uric Acid