Zhang et al. BMC Public Health 2012, 12:419 http://www.biomedcentral.com/1471-2458/12/419 R E S E A R C H A R T I C L E Open Access A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population Qian Zhang1†, Chengqi Zhang2†, Xinhong Song3, Haiyan Lin3, Dongzhi Zhang3, Wenjia Meng1, Yongyuan Zhang1, Zhenxin Zhu1, Fang Tang2, Longjian Liu4, Xiaowei Yang5 and Fuzhong Xue1* 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. Background Metabolic syndrome (MetS) refers to a combination of med- ical disorders ,including obesity, dyslipidemia, hypertension and insulin resistance[1]. When occurring together, they in- crease the risk of developing cardiovascular disease (CVD) and diabetes [2]. Recent studies indicate a wider range of bio- markers of MetS, e.g., alanine aminotransferase, white blood cell count, gamma-glutamyltranspeptidase, and serum uric acid (UA) [3, 4]. Based on cross-sectional studies, both posi- tive and negative association between UA levels and MetS has been reported [5-7]. To overcome the inherent limitation * Correspondence: xuefzh@sdu.edu.cn †Equal contributors 1Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, PO Box 100, Jinan 250012, China Full list of author information is available at the end of the article of cross-sectional design, a prospective cohort study was conducted recently, which suggested that higher serum UA level would strongly increase the risk of MetS incidence [8]. Nonetheless, such a cohort study with only baseline UA used as predictor is still limited due to the fact that UA level would vary from time to time during the lifespan of most individuals [8]. Therefore, to fully understand the relationship between UA and MetS, and further assess the specific associ- ation between UA and each medical disorder in defining MetS, longitudinal studies are expected especially those with large sample sizes and repeated measures of UA. A longitu- dinal study is usually a cohort study that involves repeated observations of the same set of variables over a period of time. Unlike cross-sectional studies or cohort studies with only baseline measures, a longitudinal study not only tracks each participant’s outcome (e.g., MetS) but also repeatedly © 2012 Zhang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.