Dongni Chen1, Haiying Zhang2, Yong Gao3, Zheng Lu4, Ziting Yao1, Yonghua Jiang1, Xinggu Lin1, Chunlei Wu5, Xiaobo Yang6, Aihua Tan7, Zengnan Mo8. 1. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Guangxi Key Laboratory of Genomic and Personalized Medicine, Nanning, Guangxi 530021, China; Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, Guangxi 530021, China. 2. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Guangxi Key Laboratory of Genomic and Personalized Medicine, Nanning, Guangxi 530021, China; Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, Guangxi 530021, China; Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China. 3. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China. 4. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China. 5. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Deprtment of Urology, First Affiliated Hospital of Xinxiang Medical College, Xinxiang, Henan Province, China. 6. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China. 7. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Department of Chemotherapy, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China. Electronic address: tanaihua2010@gmail.com. 8. Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi 530021, China; Guangxi Key Laboratory of Genomic and Personalized Medicine, Nanning, Guangxi 530021, China; Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, Guangxi 530021, China; Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China.
Abstract
BACKGROUND: It is controversial whether serum uric acid (SUA) is a risk factor for the prevalence of metabolic syndrome (MetS). The current study was designed to highlight the association of SUA and MetS and its components. METHODS: Data on 3675 healthy male subjects, aged 17-88 years, were collected for the cross-sectional study. A representative sample of 2575 individuals who did not suffer from MetS at baseline was involved in the cohort study. A cox regression model was applied to evaluate causality for the 2- and 4-year large scale longitudinal study. RESULTS: In the cross-sectional analysis, SUA showed a statistically significant negative correlation with high-density lipoprotein cholesterol (HDL-c) and a positive correlation with blood pressure (BP), triglycerides (TG), waist circumference (WC), and body mass index (BMI) (all P<0.001). In longitudinal analysis, examining the risk of developing MetS, SUA concentrations (hazard ratios comparing fourth quartile to the first quartile of 1.75; 95% CI, 1.26-2.41) were positively associated with incident MetS after adjusted for age, blood pressure, glucose, TG, HDL-c, smoking, alcohol drinking and education. CONCLUSION: SUA is positively correlated with the prevalence of MetS. Increased SUA concentration may be an independent risk factor for MetS.
BACKGROUND: It is controversial whether serum uric acid (SUA) is a risk factor for the prevalence of metabolic syndrome (MetS). The current study was designed to highlight the association of SUA and MetS and its components. METHODS: Data on 3675 healthy male subjects, aged 17-88 years, were collected for the cross-sectional study. A representative sample of 2575 individuals who did not suffer from MetS at baseline was involved in the cohort study. A cox regression model was applied to evaluate causality for the 2- and 4-year large scale longitudinal study. RESULTS: In the cross-sectional analysis, SUA showed a statistically significant negative correlation with high-density lipoprotein cholesterol (HDL-c) and a positive correlation with blood pressure (BP), triglycerides (TG), waist circumference (WC), and body mass index (BMI) (all P<0.001). In longitudinal analysis, examining the risk of developing MetS, SUA concentrations (hazard ratios comparing fourth quartile to the first quartile of 1.75; 95% CI, 1.26-2.41) were positively associated with incident MetS after adjusted for age, blood pressure, glucose, TG, HDL-c, smoking, alcohol drinking and education. CONCLUSION:SUA is positively correlated with the prevalence of MetS. Increased SUA concentration may be an independent risk factor for MetS.