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Dietary patterns associated hyperuricemia among Chinese aged 45 to 59 years

Dietary patterns associated hyperuricemia among Chinese aged 45 to 59 years In our literature research, we have not found any study reporting the association between the major dietary patterns and the risk of hyperuricemia in a middle-aged Chinese population. Herein, the present study aimed to evaluate the association of dietary patterns with the risk of hyperuricemia in the city of Hangzhou, Zhejiang Province, East China. We included 1204 participants (743 males and 461 females) aged 45 to 59 years in the present cross-sectional study. Dietary intake was assessed using a semiquantitative food frequency questionnaire in 2014 to 2016. All biochemical data and anthropometric measurements were collected following standardized procedures. Dietary patterns were determined by using factor analysis. We examined the associations between major dietary patterns and hyperuricemia risk by log-binominal regression analysis, and the results are presented as prevalence ratio (PR) and confidence interval (CI). Three major dietary patterns were identified by means of factor analysis: traditional Chinese, meat food, and mixed food patterns. After controlling for potential confounders, subjects in the highest quartile of the traditional Chinese pattern scores had a lower PR for hyperuricemia (PR=0.82; 95%CI: 0.426–0.922), in comparison to those from the lowest quartile, while compared with the lowest quartile of the meat food pattern, the highest quartile had a greater PR for hyperuricemia (PR=1.48; 95% CI: 1.120–2.097). Besides, no association was observed between mixed food pattern and the risk of hyperuricemia. Our findings indicate that the traditional Chinese pattern is associated with a decreased risk of hyperuricemia, and the meat food pattern is associated with an increased risk of hyperuricemia, whereas the mixed food pattern shows no association with the risk of hyperuricemia. Further large prospective studies are warranted to confirm our findings. Abbreviations: ALT = alanine aminotransferase, AST = asparagine aminotransferase, BMI = body mass index, CI = confidence interval, FFQ = food frequency questionnaire, FPG = fasting plasma glucose, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, PR = prevalence ratios, SUA = serum uric acid, TC = total cholesterol, TG = triglyceride, WC = waist circumference. Keywords: dietary patterns, factor analysis, hyperuricemia, middle-aged population women) had rarely been studied in China due to its relative 1. Introduction [1] unimportance. However, during the past several decades, with Hyperuricemia is a purine metabolic disorder known as a the rapid economic growth and associated lifestyle changes in precursor of gout. During the early 1980s, hyperuricemia (serum China, the prevalence of hyperuricemia has increased dramati- uric acid [SUA] >420mmol/L for men, and >360mmol/L for [2] cally. Hyperuricemia is commonly recognized as a risk factor for some chronic diseases (e.g., diabetes, hypertension, metabolic Editor: Daryle Wane. [3–6] syndrome, and chronic kidney disease). Similarly, to our Authors’ contributions: FH and XLY conceived and designed the experiments. HF knowledge, it is also considered as a multifactorial chronic and LLW conducted research. XLY analyzed data and wrote the paper. All disease that may be related to some factors, including alcohol authors read and approved the final manuscript. consumption, genetic and environmental factors, and especially Funding: This study was supported by the medical platform projects of Zhejiang [7,8] dietary factors. Province (grant no. 2016ZDA001) and Natural Science Foundation of Zhejiang Recent epidemiological studies reporting the association (grant no. Y17H030031). between diet and hyperuricemia have focused on the intakes The authors have no conflicts of interest to disclose. [8–10] a of single foods, nutrients, and food components. However, Department of Clinical Nutrition, School of Medicine, Second Affiliated Hospital in reality, people do not eat isolated nutrients but consume meals of Zhejiang University, Shangcheng District, Department of Nutrition, Zhejiang Hospital, Xihu District, Hangzhou, Zhejiang, People’s Republic of China. containing many combinations of different foods and [11] Correspondence: Xiao-Long Yu, Zhejiang Hospital, Hangzhou, Zhejiang China nutrients. In this context, dietary pattern analysis has emerged (e-mail: rainboy2018@163.com). in nutritional epidemiology as an alternative approach for Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc. examining the relationship between diet and chronic diseases, This is an open access article distributed under the terms of the Creative and it considers the combined effects of foods and potentially Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is facilitate nutritional recommendations on eating practices such as permissible to download, share, remix, transform, and buildup the work provided [12] healthy food choice for preventive disease. it is properly cited. The work cannot be used commercially without permission from the journal. Previous studies on dietary patterns from the Chinese population have mostly reported the link with obesity, Medicine (2017) 96:50(e9248) [13–15] hypertension, and diabetes. To date, only 2 epidemiological Received: 1 June 2017 / Received in final form: 24 October 2017 / Accepted: 21 November 2017 studies have reported the associations between dietary patterns [16,17] and hyperuricemia risk. Furthermore, to our knowledge, no http://dx.doi.org/10.1097/MD.0000000000009248 1 He et al. Medicine (2017) 96:50 Medicine previous study has examined the major dietary patterns in factors) were analyzed. The higher the factor loading of a food relation to hyperuricemia risk in a middle-aged Chinese group, the greater the contribution of that group to the pattern. In population. Therefore, in this study, we aimed to identify the determining the number of factors to retain, the eigenvalue and [18] major dietary patterns and assess the association between dietary scree plot were applied. In our analyses, factors with Eigen patterns and the risk of hyperuricemia among adults aged 45 to values ≥1.5 were extracted and then scree plots were used to 59 years in China. identify the major dietary patterns. Labeling of dietary patterns was based on the interpretation of foods with high factor loadings [19] for each dietary pattern. A factor score obtained for each 2. Subjects and methods participant was calculated by summing the consumption of each food group that were weighted by factor loading, the higher score 2.1. Study population showing intake of more food groups associated with that This study was carried out in Hangzhou, the capital of Zhejiang respective pattern. Finally, only food groups with absolute factor Province, east China from January 2014 to June 2016. The study loading ≥0.3 were considered to be important contributors to this sample was taken from 10 areas (Xihu, Gongshu, Shangcheng, pattern and included in the present study. Xiacheng, Bingjiang, Jianggan, Xiaoshan, Yuhang, Fuyang, and Linan) and 3 counties (Tonglu, Chunan, and Jiande) by a 2.4. Assessment of biomarker stratified cluster random-sampling method. We chose 1 residen- tial village or community from every county or area randomly, A blood sample was drawn between 7:00 and 9:00 in the according to resident health records, with participants aged morning into evacuated tubes after fasting overnight (12h). After between 45 and 59 years residing in the selected villages or blood samples were taken, serum was separated by centrifugation communities. A total of 1353 eligible participants (743 males and for 10minutes at 3000 rpm. Then samples were analyzed in the 461 females) who received health examination at the Medical Medical Center for Physical Examination, Zhejiang Hospital and Center for Physical Examination, Zhejiang Hospital and Second the Second Affiliated Hospital of Zhejiang University for fasting Affiliated Hospital of Zhejiang University in 2014 and 2016 were plasma glucose, triglyceride, total cholesterol, high-density recruited. We excluded 56 participants because of missing or lipoprotein cholesterol, low-density lipoprotein cholesterol, incomplete dietary information in their questionnaires, and 68 SUA, alanine aminotransferase, and asparagine aminotransferase participants who were taking medications for gout or hyperuri- by using an autoanalyzer (the Hitachi 7180 auto-analyzer, cemia. Besides, we further excluded 25 participants who self- Tokyo, Japan). reported a family history of hyperuricemia. Finally, 1204 participants were included in our analyses. Written informed 2.5. Assessment of other variables consent was obtained from all participants, and the protocol was approved by the institutional review and ethics committee of Data about physical activity were obtained by using a validated [14] Zhejiang Hospital and the Second Affiliated Hospital of Zhejiang self-reported questionnaire and expressed as metabolic University. equivalents in hours per week (MET-h/week). Information on smoking status was collected and categorized into never smokers, current smokers, and former smokers. The educational level was 2.2. Assessment of dietary intake classified as follows: primary school or below, middle and high Dietary intake of 56 food items was assessed by a trained school, and junior college or above. Total energy intake was dietician using a validated, semiquantitative food frequency estimated through the semiquantitative FFQ, expressed in [14] questionnaire (FFQ) described previously, which is designed kilocalorie per day (kcal/day). to assess average food intake over the previous year. This FFQ included foods that were frequently consumed by a middle-aged 2.6. Assessment of blood pressure Chinese. For each food item, subjects were asked to report their average frequency of consumption over the past year and the For blood pressure measurements, subjects were first asked to rest estimated portion size, using local weight units (1 Liang=50g) or for 10 minutes. Then, a well-trained nurse measured blood natural units (cups). Moreover, the frequency of each food item pressure using a standard mercury sphygmomanometer with the was classified as follows: never or occasionally, 1 to 3times/ subjects in the sitting position, and thereafter the mean of 3 month, 1 to 2times/week, 3 to 4times/week, 5 to 6times/week, 1 measurements was considered as the subject’s blood pressure in time/day, 2times/day, and 3times/day. Then, the selected our analyses. frequency category for each food item was converted to a daily intake and used in the further analysis. 2.7. Definition of terms Hyperuricemia was defined as SUA ≥420mmol/L (7.0mg/dL) for 2.3. Identification of dietary patterns [1] men, and ≥360mmol/L (6.0mg/dL) for women. Body mass The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy index (BMI) was calculated as weight (kilogram)/height (meter) . and the Bartlett’s test of sphericity were used to evaluate the Waist circumstance (WC) was measured at the end of normal adequacy of correlation matrices with the data. Factor analysis expiration in duplicate on bare skin midway between the lowest [14] (principal component) was used to derive the major dietary rib and the superior border of the iliac crest. Blood pressure patterns based on the frequency of consumption of 56 food was measured by using an automated sphygmomanometer with groups in this FFQ. In this method, all variables were considered the subjects in sitting position. Obesity was defined by BMI ≥28 simultaneously, each one related to the others. The factors were kg/m and abdominal adiposity was defined as a WC ≥85cm for [20] rotated using varimax rotation to achieve orthogonal (uncorre- men and ≥80cm for women in a Chinese population. lated) factors, which are easier to interpret. Factor loadings (e.g., Hypertension was defined as a systolic pressure of ≥140 mm [21] measurements of correlations between each variable and the Hg and/or a diastolic pressure of ≥90 mm Hg. 2 He et al. Medicine (2017) 96:50 www.md-journal.com 2.8. Statistical analyses There were significant differences between participants with and without hyperuricemia by gender, smoking status, economic Quartiles based on factor scores were determined for each dietary income, SUA level, obesity, and hypertension. pattern (the highest category and the lowest category represented Both the Kaiser–Meyer–Olkin index (0.755) and Bartlett’s test high and low intake of each dietary pattern, respectively). The (P<.001) showed that the correlation among the variables was characteristics of study participants were calculated across [19] sufficiently strong for a factor analysis. Factor analysis quartiles of each dietary pattern score. The data for continuous revealed 3 major dietary patterns. The first, labeled traditional variables were generally reported as the mean±SD, and the data Chinese dietary pattern was loaded by a high intake of rice and for categorical variables were reported as sum (percentages). The 2 rice products, coarse grains, starchy tubers, vegetables, pickled x test was used to assess the difference for categorical variables, vegetables, pork, soybean and soybean products, and tea. The while the analysis of variance (ANOVA) was used to describe second, labeled meat food dietary pattern was characterized by mean differences for continuous variables. Age was included as a high intakes of poultry, beef/mutton, processed and cooked meat, continuous variable. The potential confounding variables that eggs, fats/oil, snacks and fast food, milk and dairy, cake and were considered were gender (male/female), age (continuous), biscuits, and soft drinks. The third, labeled mixed dietary pattern education level (<high school, high school, and >high school), was characterized by high intakes of wheat and wheat products, physical activity level (light, moderate, and heavy), smoking vegetables, mushroom, fresh fruits, pork, fish and shrimps, status (never, current, and former), alcohol use (g/d), hyperten- seafood, and caffeinated beverages. Overall, these 3 factors sion (yes/no), BMI (continuous), and total energy intake (kcal/d). explained 28.1% of the entire variance. Moreover, the factor- After adjustment for potential confounders, log-binominal loading matrixes for 3 dietary patterns are presented in Table 2. regression analysis was used to assess the relation between The characteristics of the study participants by quartile (Q) dietary patterns and hyperuricemia risk. All statistical analyses categories of dietary pattern scores in Hangzhou are shown in were carried out with the use of the SPSS software package Table 3. Compared with participants in the lowest quartile, those version 20.0 for Windows (SPSS Inc, Chicago, IL), and a 2-tailed in the highest quartile of the traditional Chinese dietary pattern P<.05 was considered significant. were more likely to be female, older, nonsmokers, and had lower prevalence of obesity, hypertension and hyperuricemia, lower 3. Results BMI, WC, and waist–hip ratio, and higher income. Besides, in comparison with the participants from the lowest quartile of the Overall prevalence of hyperuricemia in this population was meat food dietary pattern, those in the highest quartile were more 20.2%, with male was 16.0% and female was 4.2%. The likely to be younger, male, smokers, and had higher prevalence of demographic and clinical characteristics of participants accord- obesity, hypertension and hyperuricemia, and higher BMI and ing to with and without hyperuricemia are shown in Table 1. Table 1 Demographic and clinical characteristics of participants in the Hangzhou Nutrition and Health Study. Variables Participants with hyperuricemia n= 243 Participants without hyperuricemia n= 961 Significance Demographic Age, years 50.79± 4.65 51.21± 4.69 P= .289 Gender Male 193 (79.4) 550 (57.2) X = 40.425 Female 50 (20.6) 411 (42.8) P= .000 Smoking status, % Never 137 (56.4) 684 (71.2) X = 23.285 Former 11 (4.4) 15 (1.6) P= .000 Current 95 (39.2) 262 (27.1) Education, % <High school 49 (20.2) 223 (23.2) X = 2.093 High school 70 (29.8) 296 (30.8) P= .351 >High school 124 (51.0) 442 (46.0) Monthly income per person, % 2000 RMB 57 (23.5) 346 (36.0) X = 13.730 2000–4000 RMB 107 (44.0) 357 (37.1) P= .001 >4000 RMB 79 (32.5) 258 (26.9) Physical activity, % Light 186 (76.5) 678 (70.6) X = 4.930 Moderate 42 (17.3) 183 (19.0) P= .085 Vigorous 15 (6.2) 100 (10.4) Clinical characteristics SUA, mmol/L 458.16± 56.62 302.42± 60.11 P= .000 Obesity, % 47 (19.3) 114 (11.9) X = 9.365 P= .002 Hypertension, % 105 (43.2) 300 (31.2) X = 12.496 P= .000 Categorical variables are presented as sum and percentages, and continuous variables are presented as mean± SD. RMB= Ren min bi, SUA= serum uric acid. P values for continuous variables (analysis of variance) and for categorical variables (x test). 3 He et al. Medicine (2017) 96:50 Medicine Table 2 Table 4 Factor-loading matrix for 3 major dietary patterns among 1204 Multivariable adjusted PR (95%CI) for hyperuricemia across the Chinese adult aged 45 to 59 years . quartile (Q) categories of dietary pattern scores in Zhejiang Province, China. Dietary patterns Model 1 Model 2 Model 3 Food groups Traditional Chinese Meat food Mixed food PR (95%CI) PR (95%CI) PR (95%CI) Rice and rice products 0.59 –– Traditional Chinese pattern score Wheat and wheat products –– 0.42 Q1 1.00 1.00 1.00 Q2 0.74 (0.618, 1.074) 0.85 (0.628, 1.153) 0.88 (0.703, 1.122) Coarse grains 0.48 –– Q3 0.68 (0.488, 0.936) 0.77 (0.603,1.191) 0.94 (0.673, 1.320) Starchy tubers 0.63 –– Q4 0.46 (0.264, 0.665) 0.57 (0.318, 0.725) 0.82 (0.426, 0.922) Vegetables 0.65 – 0.40 P for trend <.01 <.05 <.05 Pickled vegetables 0.58 –– Meat food pattern score Q1 1.00 1.00 1.00 Mushroom –– 0.43 Q2 1.66 (1.188, 2.365) 1.54 (1.102, 2.107) 1.03 (0.764, 1.382) Fresh fruits –– 0.52 Q3 1.79 (1.244, 2.575) 1.67 (1.191, 2.345) 1.26 (1.066, 1.792) Pork 0.66 – 0.37 Q4 2.15 (1.432, 3.763) 1.79 (1.277, 2.589) 1.48 (1.120, 2.097) Poultry – 0.50 – P for trend <.001 <.01 <.05 Mixed food pattern score Beef/mutton – 0.64 – Q1 1.00 1.00 1.00 Processed and cooked meat – 0.52 – Q2 0.97 (0.780, 1.206) 0.76 (0.621, 1.207) 0.94 (0.673, 1.320) Fish and shrimps –– 0.51 Q3 0.84 (0.660, 1.106) 0.88 (0.625, 1.225) 1.08 (0.755, 1.724) Seafood –– 0.49 Q4 0.87 (0.616,1.253) 1.03 (0.745, 1.627 1.24 (0.925,1.835) P for trend .326 .437 .650 Eggs – 0.48 – Soybean and soybean products 0.40 –– Model 1: adjusted for gender (male/female) and age (continuous). Model 2: further adjusted for Fats/oil – 0.36 – education level (<high school, high school, >high school), physical activity level (light, moderate, Snacks and fast food – 0.53 – heavy), smoking status (never, current, former), alcohol use (g/d), and hypertension (yes/no). Model 3: Milk and dairy – 0.45 – additionally adjusted for BMI and total energy intake. Cakes and biscuits – 0.56 – CI= confidence interval, PR= prevalence ratio, Q4 = the highest quartile of dietary patterns, Q1 = the lowest quartile of dietary patterns (reference). Caffeinated beverages –– 0.35 Soft drinks – 0.39 – Tea 0.31 –– The association between dietary patterns and the risk of Variance of intake explained, % 10.3 9.5 8.3 hyperuricemia by log-binomial regression is presented in Table 4. Absolute values <0.3 were excluded for simplicity. After controlling for potential confounders, participants in the highest quartile of the traditional Chinese dietary pattern scores WC. Similarly, participants in the highest quartile of the mixed had lower prevalence ratio (PR) for hyperuricemia (PR=0.82; dietary pattern were more likely to be younger, male, smokers 95% confidence interval [CI]: 0.426–0.922, P<.05) than did with higher education level and prevalence of obesity than those those in the lowest quartile, whereas those in the highest quartile in the lowest quartile. of the meat food dietary pattern score had greater PR for Table 3 Characteristics of the study participants by quartile (Q) categories of dietary pattern scores in Hangzhou. Traditional Chinese Meat food Mixed food Q1(n= 301) Q4(n= 301) P value Q1(n= 301) Q4(n= 301) P value Q (n= 301) Q4(n= 301) P value Age, years 50.0± 0.2 51.9± 0.3 <.001 51.5± 0.2 49.7± 0.2 <.001 51.8± 0.3 50.3± 0.2 <.001 BMI, kg/m 25.33± 2.85 24.11± 2.86 <.01 24.27± 2.81 25.10± 3.12 <.05 24.39± 3.11 24.93± 2.96 .225 WC, cm 87.68± 8.92 82.65± 8.35 <.01 84.05± 8.61 87.05± 8.24 <.01 84.79± 9.03 85.78± 8.65 .577 WHR 0.89± 0.06 0.86± 0.08 <.05 0.87± 0.08 0.89± 0.06 .068 0.87± 0.06 0.88± 0.06 .683 Obesity, % 50 (16.7) 27 (8.9) <.01 35 (11.6) 55 (18.3) <.05 36 (12.0) 55 (18.3) <.05 Hypertension, % 110 (36.5) 76 (25.2) <.01 72 (23.8) 98 (32.6) <.05 95 (31.6) 105 (34.9) .387 Hyperuricemia, % 59 (19.6) 40 (13.4) <.05 49 (16.4) 75 (24.8) <.01 61 (20.4) 65 (21.6) .689 Gender, % <.001 <.05 <.001 Male 186 (61.8) 110 (36.5) 161 (53.5) 189 (62.8) 151 (50.2) 204 (67.8) Female 115 (38.2) 191 (63.5) 140 (46.5) 112 (37.3) 150 (49.8) 97 (32.2) Smoking status, % <.05 <.001 <.05 Never 234 (77.7) 260 (86.4) 225 (74.8) 200 (66.5) 254 (84.4) 225 (74.7) Current 49 (16.3) 25 (8.3) 48 (15.9) 88 (29.2) 43 (14.3) 71 (23.6) Former 18 (6.0) 16 (5.3) 28 (9.3) 13 (4.3) 4 (1.3) 5 (1.7) Education, % .570 <.001 <.001 < High school 80 (26.6) 70 (23.3) 65 (21.6) 60 (19.9) 103 (34.2) 49 (16.3) High school 93 (30.9) 92 (30.6) 111 (36.9) 126 (41.9) 92 (30.6) 86 (28.6) >High school 128 (42.5) 139 (46.1) 125 (41.5) 115 (38.2) 106 (35.2) 166 (55.1) The average monthly income <.05 <.05 .889 per person, % <2000 RMB 101 (33.7) 76 (25.2) 86 (28.6) 61 (20.3) 78 (25.9) 82 (27.2) 2000–4000 RMB 121 (40.1) 115 (38.2) 121 (40.2) 118 (39.2) 126 (41.9) 127 (42.2) >4000 RMB 79 (26.2) 110 (36.6) 94 (31.2) 122 (40.5) 97 (32.2) 92 (30.6) Physical activity, % .361 <.05 .109 Light 238 (79.1) 237 (78.7) 236 (78.4) 254 (84.4) 243 (80.7) 262 (87.0) Moderate 56 (18.6) 51 (16.9) 49 (16.3) 41 (13.6) 46 (15.3) 31 (10.3) Vigorous 7 (2.3) 13 (4.4) 16 (5.3) 6 (2.0) 12 (4.0) 8 (2.7) Categorical variables are presented as sum and percentages, and continuous variables are presented as mean± SD. BMI= body mass index, RMB= Ren min bi, SD = standard deviation, WC= waist circumference, WHR= waist–hip ratio. P values for continuous variables (analysis of variance) and for categorical variables (x test). 4 He et al. Medicine (2017) 96:50 www.md-journal.com hyperuricemia (PR=1.48; 95% CI: 1.120–2.097, P<.05) than was associated with an increased risk of hyperuricemia. Our did those in the lowest quartile. No statistically significant findings are consistent with a previous study suggesting that association was observed between the mixed food pattern and animal food, for example, meat and seafood consumption, found hyperuricemia risk. among populations participating in the Health Professionals Follow-up Study, was significantly associated with an increased [29] risk of gout. In the Health Professionals Follow-up Study, 4. Discussion [29] Choi et al found that the multivariate relative risk of gout In this study, we derived 3 major dietary patterns by means of among men in the highest quintile of meat intake, as compared factor analysis: the traditional Chinese, the meat food, and the with those in the lowest quintile, was 1.41 (95%CI: 1.07–1.86; P mixed patterns. The results of this study indicate that the for trend=.02).The unfavorable effect of animal food pattern traditional Chinese pattern is associated with a decreased risk of could be attributable to this pattern’s unhealthy constituents hyperuricemia, whereas the meat food pattern is associated with (e.g., meat, seafood, and soft drinks). Meat and seafood often an elevated risk of hyperuricemia among a Chinese population contain a high content purine, which is positively associated with [30,31] aged 45 to 59 years. Besides, no significant association is found the risk of hyperuricemia. In addition, high consumption of [14] between the mixed dietary pattern and hyperuricemia risk, even meat has been associated with an increased risk of obesity. after adjusting for potential confounders. To the best of our Epidemiological studies have demonstrated a strong correlation [32,33] knowledge, this is the first study in a middle-aged Chinese between obesity and hyperuricemia. Furthermore, soft population to examine the association of major dietary patterns drinks contain large amount of fructose, which can play a role as with the risk of hyperuricemia. a source of intracellular uric acid production and results in [34] In our analyses, consumption of a traditional Chinese dietary increased serum uric acid level. Some epidemiological studies pattern, characterized by high intake of rice and rice products, have suggested that fructose intake may contribute to increased [35,36] coarse grains, starchy tubers, vegetables, pickled vegetables, risk of hyperuricemia. pork, soybean and soybean products, and tea, was associated The mixed food pattern was characterized by high intake of with a decreased risk of hyperuricemia. Compared with those in wheat and wheat products, vegetables, mushroom, fresh fruits, the lowest quartile of the traditional Chinese pattern scores, pork, fish and shrimps, seafood, and caffeinated beverages. In the participants in the highest quartile of intake had a lower PR for present study, we found no significant association between this hyperuricemia (13.4% vs 19.6%). Our findings are consistent pattern and hyperuricemia, though the prevalence of hyperurice- with prior studies, suggesting that the healthy dietary pattern is mia for the highest category of this pattern was higher compared [16] associated with a decreased risk of hyperuricemia. Zhang with the lowest category (21.6% vs 20.4%). The complex nature [16] et al reported decreased odds of hyperuricemia in Chinese of this pattern may explain this finding to some extent. On the adults who scored higher on a “soybean products and fruit” one hand, fruits and vegetables in the mixed dietary pattern are pattern (high consumption of soybean products, fruits, vegeta- considered as a source of antioxidants such as vitamin C. bles, and starchy tubers). In their analyses, the results showed that Previous studies have found that vitamin C is associated with a [24,25] OR for the top tertile of score for the “soybean products and decreased risk of hyperuriemia. Besides, evidence from fruit” pattern was 0.32 (95%CI: 0.19–0.57) compared with the epidemiological and experimental studies indicated that drinking lowest tertile of the “soybean products and fruit” pattern score. green tea was inversely associated with obesity, which is an [37] One possible mechanism of their apparently protective effect important risk factor for hyperuricemia. On the other hand, as against hyperuricemia is that they are good sources of mentioned above, high consumption of meat was associated with [30] antioxidants (e.g., vitamin C, vitamin E, and other carotenoids an increased risk of hyperuricemia. Finally, a null association compounds), isoflavones, and dietary fiber. First, dietary fiber has between mixed pattern and hyperuricemia could also be due to been recognized as having a potential role in binding uric acid in reverse causality. Participants with risk of hyperuricemia may [22] the gut for excretion. Some previous studies have also modify their dietary habits to reduce the intake of high purine indicated that dietary fiber intake is inversely associated with the food during a routine examination. In a word, these possibilities [9] risk of hyperuricemia. Second, fruits and vegetables in the could not be excluded in this study. traditional Chinese pattern contain large amounts of vitamin C, which has been shown to reduce oxidative stress and inflamma- 4.1. Strengths and limitations [23] [24] tion to lower uric acid synthesis. Stein et al reported that vitamin C has uricosuric properties, increasing renal fractional The present study had strengths and limitations. First, to the best clearance of uric acid, thereby reducing SUA. Besides, previous a of our knowledge, this is the first study in middle-aged Chinese to meta-analysis of vitamin C and serum uric acid concluded that assess the association between dietary patterns and hyperurice- [25] vitamin C supplementation significantly lowered SUA level. mia risk. Second, information about dietary intake were collected Third, evidence from epidemiological and experimental studies by trained dieticians during a structured interview, using a indicated that drinking green tea was inversely associated with validated semiquantitative FFQ. Thus, our results are reliable. hypertension, which is an important risk factor for hyperurice- Third, we have controlled for several potential known con- [26] mia. Finally, greater soybean and its products consumption founding factors for reliability in our analyses. Nevertheless, was associated with a lower presence of hyperuricemia in some limitations of this study need to be acknowledged. First, the [27] women. The inverse association of soy food with hyperurice- main limitation of the present study is its cross-sectional nature, mia might have been partly ascribed to isoflavones, which may which prevented us from making a causal inference. Thus, our inhibit the xanthine oxidase, oxidizing hypoxanthine and findings need to be confirmed in the future prospective study. [28] xanthine to uric acid in the purine catabolic pathway. Second, the use of principal component analysis requires several The meat food pattern, characterized by high intake of poultry, subjective decisions in the selection of included variables as well [38] beef/mutton, processed and cooked meat, eggs, fats/oil, snacks as in the detainment of number of factors to retain. Third, the and fast food, milk and dairy, cake and biscuits, and soft drinks, measurement errors in reporting diet using the FFQ affected our 5 He et al. Medicine (2017) 96:50 Medicine [13] Zheng PF, Shu L, Zhang XY, et al. 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Dietary patterns associated hyperuricemia among Chinese aged 45 to 59 years

Medicine , Volume 96 (50) – Dec 15, 2017

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Pubmed Central
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Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc.
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0025-7974
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1536-5964
DOI
10.1097/MD.0000000000009248
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Abstract

In our literature research, we have not found any study reporting the association between the major dietary patterns and the risk of hyperuricemia in a middle-aged Chinese population. Herein, the present study aimed to evaluate the association of dietary patterns with the risk of hyperuricemia in the city of Hangzhou, Zhejiang Province, East China. We included 1204 participants (743 males and 461 females) aged 45 to 59 years in the present cross-sectional study. Dietary intake was assessed using a semiquantitative food frequency questionnaire in 2014 to 2016. All biochemical data and anthropometric measurements were collected following standardized procedures. Dietary patterns were determined by using factor analysis. We examined the associations between major dietary patterns and hyperuricemia risk by log-binominal regression analysis, and the results are presented as prevalence ratio (PR) and confidence interval (CI). Three major dietary patterns were identified by means of factor analysis: traditional Chinese, meat food, and mixed food patterns. After controlling for potential confounders, subjects in the highest quartile of the traditional Chinese pattern scores had a lower PR for hyperuricemia (PR=0.82; 95%CI: 0.426–0.922), in comparison to those from the lowest quartile, while compared with the lowest quartile of the meat food pattern, the highest quartile had a greater PR for hyperuricemia (PR=1.48; 95% CI: 1.120–2.097). Besides, no association was observed between mixed food pattern and the risk of hyperuricemia. Our findings indicate that the traditional Chinese pattern is associated with a decreased risk of hyperuricemia, and the meat food pattern is associated with an increased risk of hyperuricemia, whereas the mixed food pattern shows no association with the risk of hyperuricemia. Further large prospective studies are warranted to confirm our findings. Abbreviations: ALT = alanine aminotransferase, AST = asparagine aminotransferase, BMI = body mass index, CI = confidence interval, FFQ = food frequency questionnaire, FPG = fasting plasma glucose, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, PR = prevalence ratios, SUA = serum uric acid, TC = total cholesterol, TG = triglyceride, WC = waist circumference. Keywords: dietary patterns, factor analysis, hyperuricemia, middle-aged population women) had rarely been studied in China due to its relative 1. Introduction [1] unimportance. However, during the past several decades, with Hyperuricemia is a purine metabolic disorder known as a the rapid economic growth and associated lifestyle changes in precursor of gout. During the early 1980s, hyperuricemia (serum China, the prevalence of hyperuricemia has increased dramati- uric acid [SUA] >420mmol/L for men, and >360mmol/L for [2] cally. Hyperuricemia is commonly recognized as a risk factor for some chronic diseases (e.g., diabetes, hypertension, metabolic Editor: Daryle Wane. [3–6] syndrome, and chronic kidney disease). Similarly, to our Authors’ contributions: FH and XLY conceived and designed the experiments. HF knowledge, it is also considered as a multifactorial chronic and LLW conducted research. XLY analyzed data and wrote the paper. All disease that may be related to some factors, including alcohol authors read and approved the final manuscript. consumption, genetic and environmental factors, and especially Funding: This study was supported by the medical platform projects of Zhejiang [7,8] dietary factors. Province (grant no. 2016ZDA001) and Natural Science Foundation of Zhejiang Recent epidemiological studies reporting the association (grant no. Y17H030031). between diet and hyperuricemia have focused on the intakes The authors have no conflicts of interest to disclose. [8–10] a of single foods, nutrients, and food components. However, Department of Clinical Nutrition, School of Medicine, Second Affiliated Hospital in reality, people do not eat isolated nutrients but consume meals of Zhejiang University, Shangcheng District, Department of Nutrition, Zhejiang Hospital, Xihu District, Hangzhou, Zhejiang, People’s Republic of China. containing many combinations of different foods and [11] Correspondence: Xiao-Long Yu, Zhejiang Hospital, Hangzhou, Zhejiang China nutrients. In this context, dietary pattern analysis has emerged (e-mail: rainboy2018@163.com). in nutritional epidemiology as an alternative approach for Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc. examining the relationship between diet and chronic diseases, This is an open access article distributed under the terms of the Creative and it considers the combined effects of foods and potentially Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is facilitate nutritional recommendations on eating practices such as permissible to download, share, remix, transform, and buildup the work provided [12] healthy food choice for preventive disease. it is properly cited. The work cannot be used commercially without permission from the journal. Previous studies on dietary patterns from the Chinese population have mostly reported the link with obesity, Medicine (2017) 96:50(e9248) [13–15] hypertension, and diabetes. To date, only 2 epidemiological Received: 1 June 2017 / Received in final form: 24 October 2017 / Accepted: 21 November 2017 studies have reported the associations between dietary patterns [16,17] and hyperuricemia risk. Furthermore, to our knowledge, no http://dx.doi.org/10.1097/MD.0000000000009248 1 He et al. Medicine (2017) 96:50 Medicine previous study has examined the major dietary patterns in factors) were analyzed. The higher the factor loading of a food relation to hyperuricemia risk in a middle-aged Chinese group, the greater the contribution of that group to the pattern. In population. Therefore, in this study, we aimed to identify the determining the number of factors to retain, the eigenvalue and [18] major dietary patterns and assess the association between dietary scree plot were applied. In our analyses, factors with Eigen patterns and the risk of hyperuricemia among adults aged 45 to values ≥1.5 were extracted and then scree plots were used to 59 years in China. identify the major dietary patterns. Labeling of dietary patterns was based on the interpretation of foods with high factor loadings [19] for each dietary pattern. A factor score obtained for each 2. Subjects and methods participant was calculated by summing the consumption of each food group that were weighted by factor loading, the higher score 2.1. Study population showing intake of more food groups associated with that This study was carried out in Hangzhou, the capital of Zhejiang respective pattern. Finally, only food groups with absolute factor Province, east China from January 2014 to June 2016. The study loading ≥0.3 were considered to be important contributors to this sample was taken from 10 areas (Xihu, Gongshu, Shangcheng, pattern and included in the present study. Xiacheng, Bingjiang, Jianggan, Xiaoshan, Yuhang, Fuyang, and Linan) and 3 counties (Tonglu, Chunan, and Jiande) by a 2.4. Assessment of biomarker stratified cluster random-sampling method. We chose 1 residen- tial village or community from every county or area randomly, A blood sample was drawn between 7:00 and 9:00 in the according to resident health records, with participants aged morning into evacuated tubes after fasting overnight (12h). After between 45 and 59 years residing in the selected villages or blood samples were taken, serum was separated by centrifugation communities. A total of 1353 eligible participants (743 males and for 10minutes at 3000 rpm. Then samples were analyzed in the 461 females) who received health examination at the Medical Medical Center for Physical Examination, Zhejiang Hospital and Center for Physical Examination, Zhejiang Hospital and Second the Second Affiliated Hospital of Zhejiang University for fasting Affiliated Hospital of Zhejiang University in 2014 and 2016 were plasma glucose, triglyceride, total cholesterol, high-density recruited. We excluded 56 participants because of missing or lipoprotein cholesterol, low-density lipoprotein cholesterol, incomplete dietary information in their questionnaires, and 68 SUA, alanine aminotransferase, and asparagine aminotransferase participants who were taking medications for gout or hyperuri- by using an autoanalyzer (the Hitachi 7180 auto-analyzer, cemia. Besides, we further excluded 25 participants who self- Tokyo, Japan). reported a family history of hyperuricemia. Finally, 1204 participants were included in our analyses. Written informed 2.5. Assessment of other variables consent was obtained from all participants, and the protocol was approved by the institutional review and ethics committee of Data about physical activity were obtained by using a validated [14] Zhejiang Hospital and the Second Affiliated Hospital of Zhejiang self-reported questionnaire and expressed as metabolic University. equivalents in hours per week (MET-h/week). Information on smoking status was collected and categorized into never smokers, current smokers, and former smokers. The educational level was 2.2. Assessment of dietary intake classified as follows: primary school or below, middle and high Dietary intake of 56 food items was assessed by a trained school, and junior college or above. Total energy intake was dietician using a validated, semiquantitative food frequency estimated through the semiquantitative FFQ, expressed in [14] questionnaire (FFQ) described previously, which is designed kilocalorie per day (kcal/day). to assess average food intake over the previous year. This FFQ included foods that were frequently consumed by a middle-aged 2.6. Assessment of blood pressure Chinese. For each food item, subjects were asked to report their average frequency of consumption over the past year and the For blood pressure measurements, subjects were first asked to rest estimated portion size, using local weight units (1 Liang=50g) or for 10 minutes. Then, a well-trained nurse measured blood natural units (cups). Moreover, the frequency of each food item pressure using a standard mercury sphygmomanometer with the was classified as follows: never or occasionally, 1 to 3times/ subjects in the sitting position, and thereafter the mean of 3 month, 1 to 2times/week, 3 to 4times/week, 5 to 6times/week, 1 measurements was considered as the subject’s blood pressure in time/day, 2times/day, and 3times/day. Then, the selected our analyses. frequency category for each food item was converted to a daily intake and used in the further analysis. 2.7. Definition of terms Hyperuricemia was defined as SUA ≥420mmol/L (7.0mg/dL) for 2.3. Identification of dietary patterns [1] men, and ≥360mmol/L (6.0mg/dL) for women. Body mass The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy index (BMI) was calculated as weight (kilogram)/height (meter) . and the Bartlett’s test of sphericity were used to evaluate the Waist circumstance (WC) was measured at the end of normal adequacy of correlation matrices with the data. Factor analysis expiration in duplicate on bare skin midway between the lowest [14] (principal component) was used to derive the major dietary rib and the superior border of the iliac crest. Blood pressure patterns based on the frequency of consumption of 56 food was measured by using an automated sphygmomanometer with groups in this FFQ. In this method, all variables were considered the subjects in sitting position. Obesity was defined by BMI ≥28 simultaneously, each one related to the others. The factors were kg/m and abdominal adiposity was defined as a WC ≥85cm for [20] rotated using varimax rotation to achieve orthogonal (uncorre- men and ≥80cm for women in a Chinese population. lated) factors, which are easier to interpret. Factor loadings (e.g., Hypertension was defined as a systolic pressure of ≥140 mm [21] measurements of correlations between each variable and the Hg and/or a diastolic pressure of ≥90 mm Hg. 2 He et al. Medicine (2017) 96:50 www.md-journal.com 2.8. Statistical analyses There were significant differences between participants with and without hyperuricemia by gender, smoking status, economic Quartiles based on factor scores were determined for each dietary income, SUA level, obesity, and hypertension. pattern (the highest category and the lowest category represented Both the Kaiser–Meyer–Olkin index (0.755) and Bartlett’s test high and low intake of each dietary pattern, respectively). The (P<.001) showed that the correlation among the variables was characteristics of study participants were calculated across [19] sufficiently strong for a factor analysis. Factor analysis quartiles of each dietary pattern score. The data for continuous revealed 3 major dietary patterns. The first, labeled traditional variables were generally reported as the mean±SD, and the data Chinese dietary pattern was loaded by a high intake of rice and for categorical variables were reported as sum (percentages). The 2 rice products, coarse grains, starchy tubers, vegetables, pickled x test was used to assess the difference for categorical variables, vegetables, pork, soybean and soybean products, and tea. The while the analysis of variance (ANOVA) was used to describe second, labeled meat food dietary pattern was characterized by mean differences for continuous variables. Age was included as a high intakes of poultry, beef/mutton, processed and cooked meat, continuous variable. The potential confounding variables that eggs, fats/oil, snacks and fast food, milk and dairy, cake and were considered were gender (male/female), age (continuous), biscuits, and soft drinks. The third, labeled mixed dietary pattern education level (<high school, high school, and >high school), was characterized by high intakes of wheat and wheat products, physical activity level (light, moderate, and heavy), smoking vegetables, mushroom, fresh fruits, pork, fish and shrimps, status (never, current, and former), alcohol use (g/d), hyperten- seafood, and caffeinated beverages. Overall, these 3 factors sion (yes/no), BMI (continuous), and total energy intake (kcal/d). explained 28.1% of the entire variance. Moreover, the factor- After adjustment for potential confounders, log-binominal loading matrixes for 3 dietary patterns are presented in Table 2. regression analysis was used to assess the relation between The characteristics of the study participants by quartile (Q) dietary patterns and hyperuricemia risk. All statistical analyses categories of dietary pattern scores in Hangzhou are shown in were carried out with the use of the SPSS software package Table 3. Compared with participants in the lowest quartile, those version 20.0 for Windows (SPSS Inc, Chicago, IL), and a 2-tailed in the highest quartile of the traditional Chinese dietary pattern P<.05 was considered significant. were more likely to be female, older, nonsmokers, and had lower prevalence of obesity, hypertension and hyperuricemia, lower 3. Results BMI, WC, and waist–hip ratio, and higher income. Besides, in comparison with the participants from the lowest quartile of the Overall prevalence of hyperuricemia in this population was meat food dietary pattern, those in the highest quartile were more 20.2%, with male was 16.0% and female was 4.2%. The likely to be younger, male, smokers, and had higher prevalence of demographic and clinical characteristics of participants accord- obesity, hypertension and hyperuricemia, and higher BMI and ing to with and without hyperuricemia are shown in Table 1. Table 1 Demographic and clinical characteristics of participants in the Hangzhou Nutrition and Health Study. Variables Participants with hyperuricemia n= 243 Participants without hyperuricemia n= 961 Significance Demographic Age, years 50.79± 4.65 51.21± 4.69 P= .289 Gender Male 193 (79.4) 550 (57.2) X = 40.425 Female 50 (20.6) 411 (42.8) P= .000 Smoking status, % Never 137 (56.4) 684 (71.2) X = 23.285 Former 11 (4.4) 15 (1.6) P= .000 Current 95 (39.2) 262 (27.1) Education, % <High school 49 (20.2) 223 (23.2) X = 2.093 High school 70 (29.8) 296 (30.8) P= .351 >High school 124 (51.0) 442 (46.0) Monthly income per person, % 2000 RMB 57 (23.5) 346 (36.0) X = 13.730 2000–4000 RMB 107 (44.0) 357 (37.1) P= .001 >4000 RMB 79 (32.5) 258 (26.9) Physical activity, % Light 186 (76.5) 678 (70.6) X = 4.930 Moderate 42 (17.3) 183 (19.0) P= .085 Vigorous 15 (6.2) 100 (10.4) Clinical characteristics SUA, mmol/L 458.16± 56.62 302.42± 60.11 P= .000 Obesity, % 47 (19.3) 114 (11.9) X = 9.365 P= .002 Hypertension, % 105 (43.2) 300 (31.2) X = 12.496 P= .000 Categorical variables are presented as sum and percentages, and continuous variables are presented as mean± SD. RMB= Ren min bi, SUA= serum uric acid. P values for continuous variables (analysis of variance) and for categorical variables (x test). 3 He et al. Medicine (2017) 96:50 Medicine Table 2 Table 4 Factor-loading matrix for 3 major dietary patterns among 1204 Multivariable adjusted PR (95%CI) for hyperuricemia across the Chinese adult aged 45 to 59 years . quartile (Q) categories of dietary pattern scores in Zhejiang Province, China. Dietary patterns Model 1 Model 2 Model 3 Food groups Traditional Chinese Meat food Mixed food PR (95%CI) PR (95%CI) PR (95%CI) Rice and rice products 0.59 –– Traditional Chinese pattern score Wheat and wheat products –– 0.42 Q1 1.00 1.00 1.00 Q2 0.74 (0.618, 1.074) 0.85 (0.628, 1.153) 0.88 (0.703, 1.122) Coarse grains 0.48 –– Q3 0.68 (0.488, 0.936) 0.77 (0.603,1.191) 0.94 (0.673, 1.320) Starchy tubers 0.63 –– Q4 0.46 (0.264, 0.665) 0.57 (0.318, 0.725) 0.82 (0.426, 0.922) Vegetables 0.65 – 0.40 P for trend <.01 <.05 <.05 Pickled vegetables 0.58 –– Meat food pattern score Q1 1.00 1.00 1.00 Mushroom –– 0.43 Q2 1.66 (1.188, 2.365) 1.54 (1.102, 2.107) 1.03 (0.764, 1.382) Fresh fruits –– 0.52 Q3 1.79 (1.244, 2.575) 1.67 (1.191, 2.345) 1.26 (1.066, 1.792) Pork 0.66 – 0.37 Q4 2.15 (1.432, 3.763) 1.79 (1.277, 2.589) 1.48 (1.120, 2.097) Poultry – 0.50 – P for trend <.001 <.01 <.05 Mixed food pattern score Beef/mutton – 0.64 – Q1 1.00 1.00 1.00 Processed and cooked meat – 0.52 – Q2 0.97 (0.780, 1.206) 0.76 (0.621, 1.207) 0.94 (0.673, 1.320) Fish and shrimps –– 0.51 Q3 0.84 (0.660, 1.106) 0.88 (0.625, 1.225) 1.08 (0.755, 1.724) Seafood –– 0.49 Q4 0.87 (0.616,1.253) 1.03 (0.745, 1.627 1.24 (0.925,1.835) P for trend .326 .437 .650 Eggs – 0.48 – Soybean and soybean products 0.40 –– Model 1: adjusted for gender (male/female) and age (continuous). Model 2: further adjusted for Fats/oil – 0.36 – education level (<high school, high school, >high school), physical activity level (light, moderate, Snacks and fast food – 0.53 – heavy), smoking status (never, current, former), alcohol use (g/d), and hypertension (yes/no). Model 3: Milk and dairy – 0.45 – additionally adjusted for BMI and total energy intake. Cakes and biscuits – 0.56 – CI= confidence interval, PR= prevalence ratio, Q4 = the highest quartile of dietary patterns, Q1 = the lowest quartile of dietary patterns (reference). Caffeinated beverages –– 0.35 Soft drinks – 0.39 – Tea 0.31 –– The association between dietary patterns and the risk of Variance of intake explained, % 10.3 9.5 8.3 hyperuricemia by log-binomial regression is presented in Table 4. Absolute values <0.3 were excluded for simplicity. After controlling for potential confounders, participants in the highest quartile of the traditional Chinese dietary pattern scores WC. Similarly, participants in the highest quartile of the mixed had lower prevalence ratio (PR) for hyperuricemia (PR=0.82; dietary pattern were more likely to be younger, male, smokers 95% confidence interval [CI]: 0.426–0.922, P<.05) than did with higher education level and prevalence of obesity than those those in the lowest quartile, whereas those in the highest quartile in the lowest quartile. of the meat food dietary pattern score had greater PR for Table 3 Characteristics of the study participants by quartile (Q) categories of dietary pattern scores in Hangzhou. Traditional Chinese Meat food Mixed food Q1(n= 301) Q4(n= 301) P value Q1(n= 301) Q4(n= 301) P value Q (n= 301) Q4(n= 301) P value Age, years 50.0± 0.2 51.9± 0.3 <.001 51.5± 0.2 49.7± 0.2 <.001 51.8± 0.3 50.3± 0.2 <.001 BMI, kg/m 25.33± 2.85 24.11± 2.86 <.01 24.27± 2.81 25.10± 3.12 <.05 24.39± 3.11 24.93± 2.96 .225 WC, cm 87.68± 8.92 82.65± 8.35 <.01 84.05± 8.61 87.05± 8.24 <.01 84.79± 9.03 85.78± 8.65 .577 WHR 0.89± 0.06 0.86± 0.08 <.05 0.87± 0.08 0.89± 0.06 .068 0.87± 0.06 0.88± 0.06 .683 Obesity, % 50 (16.7) 27 (8.9) <.01 35 (11.6) 55 (18.3) <.05 36 (12.0) 55 (18.3) <.05 Hypertension, % 110 (36.5) 76 (25.2) <.01 72 (23.8) 98 (32.6) <.05 95 (31.6) 105 (34.9) .387 Hyperuricemia, % 59 (19.6) 40 (13.4) <.05 49 (16.4) 75 (24.8) <.01 61 (20.4) 65 (21.6) .689 Gender, % <.001 <.05 <.001 Male 186 (61.8) 110 (36.5) 161 (53.5) 189 (62.8) 151 (50.2) 204 (67.8) Female 115 (38.2) 191 (63.5) 140 (46.5) 112 (37.3) 150 (49.8) 97 (32.2) Smoking status, % <.05 <.001 <.05 Never 234 (77.7) 260 (86.4) 225 (74.8) 200 (66.5) 254 (84.4) 225 (74.7) Current 49 (16.3) 25 (8.3) 48 (15.9) 88 (29.2) 43 (14.3) 71 (23.6) Former 18 (6.0) 16 (5.3) 28 (9.3) 13 (4.3) 4 (1.3) 5 (1.7) Education, % .570 <.001 <.001 < High school 80 (26.6) 70 (23.3) 65 (21.6) 60 (19.9) 103 (34.2) 49 (16.3) High school 93 (30.9) 92 (30.6) 111 (36.9) 126 (41.9) 92 (30.6) 86 (28.6) >High school 128 (42.5) 139 (46.1) 125 (41.5) 115 (38.2) 106 (35.2) 166 (55.1) The average monthly income <.05 <.05 .889 per person, % <2000 RMB 101 (33.7) 76 (25.2) 86 (28.6) 61 (20.3) 78 (25.9) 82 (27.2) 2000–4000 RMB 121 (40.1) 115 (38.2) 121 (40.2) 118 (39.2) 126 (41.9) 127 (42.2) >4000 RMB 79 (26.2) 110 (36.6) 94 (31.2) 122 (40.5) 97 (32.2) 92 (30.6) Physical activity, % .361 <.05 .109 Light 238 (79.1) 237 (78.7) 236 (78.4) 254 (84.4) 243 (80.7) 262 (87.0) Moderate 56 (18.6) 51 (16.9) 49 (16.3) 41 (13.6) 46 (15.3) 31 (10.3) Vigorous 7 (2.3) 13 (4.4) 16 (5.3) 6 (2.0) 12 (4.0) 8 (2.7) Categorical variables are presented as sum and percentages, and continuous variables are presented as mean± SD. BMI= body mass index, RMB= Ren min bi, SD = standard deviation, WC= waist circumference, WHR= waist–hip ratio. P values for continuous variables (analysis of variance) and for categorical variables (x test). 4 He et al. Medicine (2017) 96:50 www.md-journal.com hyperuricemia (PR=1.48; 95% CI: 1.120–2.097, P<.05) than was associated with an increased risk of hyperuricemia. Our did those in the lowest quartile. No statistically significant findings are consistent with a previous study suggesting that association was observed between the mixed food pattern and animal food, for example, meat and seafood consumption, found hyperuricemia risk. among populations participating in the Health Professionals Follow-up Study, was significantly associated with an increased [29] risk of gout. In the Health Professionals Follow-up Study, 4. Discussion [29] Choi et al found that the multivariate relative risk of gout In this study, we derived 3 major dietary patterns by means of among men in the highest quintile of meat intake, as compared factor analysis: the traditional Chinese, the meat food, and the with those in the lowest quintile, was 1.41 (95%CI: 1.07–1.86; P mixed patterns. The results of this study indicate that the for trend=.02).The unfavorable effect of animal food pattern traditional Chinese pattern is associated with a decreased risk of could be attributable to this pattern’s unhealthy constituents hyperuricemia, whereas the meat food pattern is associated with (e.g., meat, seafood, and soft drinks). Meat and seafood often an elevated risk of hyperuricemia among a Chinese population contain a high content purine, which is positively associated with [30,31] aged 45 to 59 years. Besides, no significant association is found the risk of hyperuricemia. In addition, high consumption of [14] between the mixed dietary pattern and hyperuricemia risk, even meat has been associated with an increased risk of obesity. after adjusting for potential confounders. To the best of our Epidemiological studies have demonstrated a strong correlation [32,33] knowledge, this is the first study in a middle-aged Chinese between obesity and hyperuricemia. Furthermore, soft population to examine the association of major dietary patterns drinks contain large amount of fructose, which can play a role as with the risk of hyperuricemia. a source of intracellular uric acid production and results in [34] In our analyses, consumption of a traditional Chinese dietary increased serum uric acid level. Some epidemiological studies pattern, characterized by high intake of rice and rice products, have suggested that fructose intake may contribute to increased [35,36] coarse grains, starchy tubers, vegetables, pickled vegetables, risk of hyperuricemia. pork, soybean and soybean products, and tea, was associated The mixed food pattern was characterized by high intake of with a decreased risk of hyperuricemia. Compared with those in wheat and wheat products, vegetables, mushroom, fresh fruits, the lowest quartile of the traditional Chinese pattern scores, pork, fish and shrimps, seafood, and caffeinated beverages. In the participants in the highest quartile of intake had a lower PR for present study, we found no significant association between this hyperuricemia (13.4% vs 19.6%). Our findings are consistent pattern and hyperuricemia, though the prevalence of hyperurice- with prior studies, suggesting that the healthy dietary pattern is mia for the highest category of this pattern was higher compared [16] associated with a decreased risk of hyperuricemia. Zhang with the lowest category (21.6% vs 20.4%). The complex nature [16] et al reported decreased odds of hyperuricemia in Chinese of this pattern may explain this finding to some extent. On the adults who scored higher on a “soybean products and fruit” one hand, fruits and vegetables in the mixed dietary pattern are pattern (high consumption of soybean products, fruits, vegeta- considered as a source of antioxidants such as vitamin C. bles, and starchy tubers). In their analyses, the results showed that Previous studies have found that vitamin C is associated with a [24,25] OR for the top tertile of score for the “soybean products and decreased risk of hyperuriemia. Besides, evidence from fruit” pattern was 0.32 (95%CI: 0.19–0.57) compared with the epidemiological and experimental studies indicated that drinking lowest tertile of the “soybean products and fruit” pattern score. green tea was inversely associated with obesity, which is an [37] One possible mechanism of their apparently protective effect important risk factor for hyperuricemia. On the other hand, as against hyperuricemia is that they are good sources of mentioned above, high consumption of meat was associated with [30] antioxidants (e.g., vitamin C, vitamin E, and other carotenoids an increased risk of hyperuricemia. Finally, a null association compounds), isoflavones, and dietary fiber. First, dietary fiber has between mixed pattern and hyperuricemia could also be due to been recognized as having a potential role in binding uric acid in reverse causality. Participants with risk of hyperuricemia may [22] the gut for excretion. Some previous studies have also modify their dietary habits to reduce the intake of high purine indicated that dietary fiber intake is inversely associated with the food during a routine examination. In a word, these possibilities [9] risk of hyperuricemia. Second, fruits and vegetables in the could not be excluded in this study. traditional Chinese pattern contain large amounts of vitamin C, which has been shown to reduce oxidative stress and inflamma- 4.1. Strengths and limitations [23] [24] tion to lower uric acid synthesis. Stein et al reported that vitamin C has uricosuric properties, increasing renal fractional The present study had strengths and limitations. First, to the best clearance of uric acid, thereby reducing SUA. Besides, previous a of our knowledge, this is the first study in middle-aged Chinese to meta-analysis of vitamin C and serum uric acid concluded that assess the association between dietary patterns and hyperurice- [25] vitamin C supplementation significantly lowered SUA level. mia risk. Second, information about dietary intake were collected Third, evidence from epidemiological and experimental studies by trained dieticians during a structured interview, using a indicated that drinking green tea was inversely associated with validated semiquantitative FFQ. Thus, our results are reliable. hypertension, which is an important risk factor for hyperurice- Third, we have controlled for several potential known con- [26] mia. Finally, greater soybean and its products consumption founding factors for reliability in our analyses. Nevertheless, was associated with a lower presence of hyperuricemia in some limitations of this study need to be acknowledged. First, the [27] women. The inverse association of soy food with hyperurice- main limitation of the present study is its cross-sectional nature, mia might have been partly ascribed to isoflavones, which may which prevented us from making a causal inference. Thus, our inhibit the xanthine oxidase, oxidizing hypoxanthine and findings need to be confirmed in the future prospective study. [28] xanthine to uric acid in the purine catabolic pathway. Second, the use of principal component analysis requires several The meat food pattern, characterized by high intake of poultry, subjective decisions in the selection of included variables as well [38] beef/mutton, processed and cooked meat, eggs, fats/oil, snacks as in the detainment of number of factors to retain. Third, the and fast food, milk and dairy, cake and biscuits, and soft drinks, measurement errors in reporting diet using the FFQ affected our 5 He et al. Medicine (2017) 96:50 Medicine [13] Zheng PF, Shu L, Zhang XY, et al. 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Published: Dec 15, 2017

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