Zhang et al. BMC Public Health 2012, 12:419 http://www.biomedcentral.com/1471-2458/12/419 measures risk factors that would change with time (e.g., UA). Therefore, longitudinal studies allow one to make observa- tions more accurately and model time-dependent relation- ships. They are more becoming popular in epidemiology and biomedical research. In this paper, we study the UA-MetS associations using data from a longitudinal study of male residents who visited the Health Management Center of Shandong Provincial Hospital (HMCSPH) for routine phys- ical examinations. Methods Study population In our longitudinal study, participants include 2222 male residents of Shandong Province who visited HMCSPH (an affiliate of organization of Shandong University) at least three times for annual physical examinations between 2005 and 2010. During the 6 year follow-up, each participant on average had 3.63 ± 0.016 measures including serum UA levels. For this study, we only included participants who had not been diagnosed as having MetS at baseline. They were all Chinese Han with baseline age between 25 and 91 years, all belong to middle and upper socioeconomic classes. This study was approved by the Ethics Committee of School of Public Health, Shandong University, and all participants gave informed written consent. Measurements Blood samples were collected from participants after an overnight fast of at least 12 hours. Height and weight were measured on participants wearing light clothing without shoes, and body mass index (BMI) was calculated as weight (kg) divided by squared height (m). Blood pressure was measured on right upper arm with participants in sit- ting position after 5 minutes rest. Peripheral blood sam- ples was obtained for measuring the following parameters: glucose, total cholesterol (CHOL), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, uric acid (UA), gamma-glutamyltranspeptidase (GGT), serum albumin (ALB), serum globulins (GLO), blood urea nitro- gen (BUN), serum creatinine (CREA), hemoglobin (Hb), hematokrit (HCT), mean corpuscular volume(MCV), mean corpuscular hemoglobin (MCH), Red blood cell dis- tribution width (RDW), white blood count (WBC), platelet distribution width (PDW), mean platelet volume (MPV), and thrombocytocrit (PCT). Data on smoking habits, alco- hol intake, diet habits, sleeping quality, exercise frequency, and other variables were obtained using standardized questionnaire. Definition of metS Considering the physiological characteristics of our target population, the diagnostic criteria recommended by Dia- betes Branch of the Chinese Medical Association (CDS) [9] was used in defining MetS in this study. An participant Page 2 of 8 was claimed as having MetS if he or she had three or more of the following four medical conditions: (1) overweight or obesity, i.e., BMI ≥25.0 Kg/M [2]; (2) hypertension, i.e., systolic blood pressure (SBC) ≥140 mmHg, or diastolic blood pressure (DBP) ≥90 mmHg, or previously diagnosed as hypertension; (3) dyslipidemia, i.e., fasting triglycerides (TG) ≥1.7 mmol/L(110 mg/dl), or fasting high-density (HDL) <0.9 mmol/L(35 mg/dl); (4) hyperglycemia was defined as fasting blood-glucose (FPG) ≥6.1 mmol/L (110 mg/dl), or 2 h Postmeal Glucose (PG) ≥7.8 mmol/L (140 mg/dl),or previously diagnosed as hyperglycemia. Missing data imputation In our longitudinal study, there were some variables (see Table 1) with missing values due to early withdrawal of the participants or missing of certain physical examinations. To handle missing values, the strategy of multiple imputation (MI) was adopted where imputations were made using the Markov Chain Monte Carlo (MCMC) algorithm imple- mented in the MI Procedure of SAS 9.1.3 [10]. Then, each imputed data set was analysed using GEE regression mod- els. Finally the multiple set of estimators for parameters of interest from the GEE analyses were combined to make final inferences. Considering that the variables in our ana- lysis were continuously distributed with arbitrary missing- ness patterns, a multivariate normal distribution model was assumed in the MI Procedure. As seen in Table 1, most of the imputed variables had less than 10 % of missingness. UA Levels and potential confounding factors Before fitting a GEE model, the original continuous serum UA measure was categorized into 4 levels using the 3 quar- tiles (P25, P50 and P75) as cut-off values. As seen in Table 1, potential confounding factors were also considered during the GEE analysis. Statistical analysis Descriptive analyses were first conducted for the distribu- tions of UA levels and potential covariates collected at the baseline survey. Then, each variable of interest was com- pared, using student’s t-test, between subjects with and subjects without MetS at each follow-up interval (i.e., each year baseline). To study the association between UA levels and MetS, we first fitted simple GEE models, each with a single predictor (i.e., the UA variable or any one of the confounding factors). We finally fitted a multiple GEE re- gression model. A covariate was added to the final mul- tiple regression model only if it was found significant (p < 0.05) in the single-predictor model. Note that the GEE modelling strategy is capable of describing time- dependent relationship between UA levels and the MetS status (yes or no), after adjusting potential confounders. All these variables are repeatedly measured during the 6- year follow-up. Age at baseline was also added to the