UA GGT ALB GLO BUN CREA CHOL Hb HCT MCV MCH RDW WBC PDW MPV PCT diet Zhang et al. BMC Public Health 2012, 12:419 http://www.biomedcentral.com/1471-2458/12/419 Table 1 Definition of UA levels and potential confounding factors and missingess rate variables Assignments qualified by their quartiles of P25, P50 and P75 every year; Q1: the UA level ≤ P25, Q2: P25 < the UA level < P50, Q3: P50 < the UA level < P75, Q4: the UA level ≥ P75 2005 2006 2007 2008 2009 2010 P25 297 P50 353 P75 406 323 372 419 302 345 392 306 349 397 307 353 401 323 368 416 gamma-glutamyltranspeptidase , U/L serum albumin, g/L serum globulins, g/L blood urea nitrogen, mg/L serum creatinine, mg/dl Total cholesterol, mg/dl Hemoglobin, g/L Hematokrit, % mean corpuscular volume, fL mean corpuscular hemoglobin, pg Red blood cell distribution width, % white blood count, 109g/L Platelet distribution width mean platelet volume, fL Thrombocytocrit, % 0: Vegetarian, 1: meat-based, 2: normal, 3: sea food drinking 0: never, 1: seldom, 2: often, wine, 3: often beer, 6 % 4: often, Chinese spirits, 5: often, mixed all kinds smoking 0: never, 1: seldom, 2: quit, 3: 1-4/d, 4: 5-15/d, 5: >15/d quality of 0: excellent, 1: well, 2: fair, 3: poor, 4: very poor 6 % sleep exercise 0: never, 1: seldom, 2: often or everyday multiple GEE analysis procedure to adjust for the age ef- fect. In each GEE model, ‘Logit’ was chosen as the link function of GEE, because either MetS status is a binary dependent variable. Following the same procedure, we studied the association between UA levels and each one of the four medical disorders in defining MetS (i.e., over- weight/obesity, hypertension, dyslipidemia, and hypergly- cemia), which were all defined as binary variables. All statistical analysis was performed by SAS 9.1.3. Results Additional file 1: Table S1 in Supplement Materials shows the distribution of UA levels and other potential confounding factors at the baseline survey, and their dis- tributions between subjects with and subjects without Missingess Rate 0 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 1 % 6 % 6 % 17 % Page 3 of 8 MetS at each follow-up interval (i.e., each year after baseline). The table shows that the UA level and other potential confounding factors in the MetS group were generally higher than their counterparts in the non- MetS group at each follow-up intervals (P < 0.05), al- though some factors were not statistically significant at some follow-up intervals. The numbers of participants at each follow-up interval during the study period (Jan 2005 - Dec 2010) are also shown in the Supplemental Materials; see Additional file 1: Table S2. Table 2 shows the results of single-predictor GEE mod- els for the UA levels and other potential confounding fac- tors. It is seen that the UA level was strongly associated with MetS (OR = 1.2952; 95 % CI = 1.1864-1.4141; p < .0001). Age, GGT, ALB, GLO, CHOL, Hb, MCV and MCH and WBC were all significant predictors or con- founders at the α = 0.05 level. Other factors including BUN, CREA, HCT, RDW, PDW, MPV, PCT, iet, drinking, smoking, quality of sleep and exercise are not significant at the α = 0.05 level. Table 3 illustrates the association between UA levels and MetS status after adjusting other potential confounding factors using the final multiple GEE model. It reveals that, compared with the lowest level of UA (Q1), the highest level of UA (Q4) was strongly associated with MetS status with a positive relationship (OR = 1.6920; 95 % CI = 1.3390-2.1381; p < 0.0001). Although not significant, the ORs for Q2 and Q3 were all larger than 1.0, and an in- creasing trend was seen in OR from Q2 to Q4. The results on association of UA levels with each single MetS component, are shown in Table 4, 5, 6 and 7 of the Supplemental Materials, respectively for overweight/obes- ity, hypertension dyslipidemia and hyperglycemia. Table 4 shows that UA was a strong independent risk factor to overweight/obesity; and an obvious increasing trend in OR is seen from Q2 to Q4 after adjusting the other three components of MetS and potential confounding factors. Table 6 and 4 jointly depict similar patterns for the associ- ation between UA levels and hypertension or dyslipide- mia, except that the OR is not significant at the Q2 level. In contrast, UA is identified as a protective factor with the range of ORs ranged from 0.58-0.74 after adjusting the other three components and the same set of potential confounding factors; see Table 7. Discussions The relationship among UA, MetS and cardiovascular dis- eases has received much attention in recent years. The findings from a nationwide representative sample of US adults indicate that the prevalence of the MetS increases substantially with increasing levels of UA [11]. UA as an independent risk predictor for MetS also was found in Ko- rean male workers aged 30–39 years [12], in Thai adults receiving annual physical examinations [13], in Japanese