A Longitudinal Cohort Based Association Study Between Uric Acid Level and Metabolic Syndrome in Chinese Han Urban Male Population

Overview

Journal BMC Public Health

Publisher Biomed Central

Specialty Public Health

Date 2012 Jun 12

PMID 22682157

Citations 31

Authors

Qian Zhang

Chengqi Zhang

Xinhong Song

Haiyan Lin

Dongzhi Zhang

Wenjia Meng

Yongyuan Zhang

Zhenxin Zhu

Fang Tang

Longjian Liu

Xiaowei Yang

Fuzhong Xue

Affiliations

Soon will be listed here.

Abstract

Background: It has been recently demonstrated that serum uric acid (UA) is associated with metabolic syndrome (MetS) or its related clinical indications based on cross-sectional or prospective cohort studies. Nonetheless, due to the fact that UA level constantly fluctuates from time to time even for the person, using a single measure of UA level at baseline of those studies may not be sufficient for estimating the UA-Mets association.

Methods: To further estimate this time-dependent association, we fitted a generalized estimating equation (GEE) regression model with data from a large-scale 6-year longitudinal study, which included 2222 participants aged > =25 years with an average of 3.5 repeated measures of UA per person in the Health Management Center of Shandong Provincial Hospital, Shandong, China.

Results: After adjusting for other potential confounding factors (i.e., total cholesterol, low-density lipoprotein), it was verified that time-dependent UA level was an independent risk factor for MetS (OR = 1.6920, p < 0.0001). It was found that UA level was positively associated with obesity, hypertension, and dyslipidemia, but was inversely associated with hyperglycemia.

Conclusions: Serum UA level may serve as an important risk factor of MetS. Additionally, our study suggested that UA level be an independent risk factor to obesity, hypertension and dyslipidemia, but a protective factor to hyperglycemia. These findings are concordant with results from other studies on Asian populations, and jointly provide a basis to further develop a risk assessment model for predicting MetS using UA levels and other factors in China.

Citing Articles

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