Gender Difference of Metabolic Syndrome and Its Association Between Dietary Diversity at Different Ages

Population and data collection Chinese Nutrition and Health Survey （CHNS） was implemented by the University of North Carolina at Chapel Hill (UNC-CH) and the Chinese Institute of Nutrition and Food Safety (INFS), and China Center for Disease Control and Prevention (CCDC). This survey was approved by the Institutional Review Board of aforementioned institutions. All participants provided the written informed consent. Detailed information about this survey and laboratory examinations has been described elsewhere .

Dietary assessment and dietary diversity score Individual daily food consumption data were collected through face-to-face interviews for 3 consecutive days which are randomly distributed within one week. Trained field interviewers helped each participants recall the food that they consumed at home and outside in a 24-h period and recorded the codes (listed in the Food Composition Table of China), types, amounts, and locations of consumption for each food item by using food models and pictures. More detailed information about the dietary data in CHNS has been previously reported (The China Health and Nutrition Survey, 1989-2011,PMID: 24341753 .).

In line with previous studies, the investigator combine the original 12 major food categories in Chinese Food Composition Table into 6 broad groups (grains, vegetables, fruits, meat/poultry/seafood, dairy, and beans/eggs/nuts) based on similarities in nutrient composition and dietary function. Detailed information about DDS has been depicted in previous study(PMID: 27848939). Foods consumed less than a minimum amount (25g) per day was excluded to avoid measurement error caused by negligible consumption. Since the consumption of dairy was generally low in Chinese dietary, the cutoff point was adjusted to 10g per day. The DDS value ranges from 0 to 6 and higher value indicates greater diversity of diet.

Outcome variables and covariates Waist circumference was measured around the body at the top of the hipbone, using an unstretched tape over the light cloth. Measurement was conducted without putting any pressure to body surface, and the value was recorded to the nearest 0.1cm.Height and weight was measured by trained health worker using regularly calibrated equipments under the instructions of manufacturers. BMI was calculated by dividing the weight (kg) by the square of the height (m2). Blood pressure was measured after thrice rest in a seated position, each rest lasts for 5 minutes. It was utilized to calculate the average value of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Blood sample was collected after 12-14h overnight fasting from all participants, and was stored in vacationer tubes. All blood samples were analyzed in the central laboratories of China-Japan Friendship Hospital. Fasting plasma glucose was measured by glucose oxidase-3'-phosphoadenosine 5'-phosphate (GOD-PAP), using the kit produced by Randox, United Kingdom (UK). Serum high-density lipoprotein cholesterol (HDL-C) concentration was measured by enzymatic method; serum triglyceride levels was measured by cholesterol oxidase peroxidase - 3'-phosphoadenosine 5'-phosphate (CHOD-PAP); these two testing reagent were produced by the Kyowa, Japan. The last three indices were measured by the Hitachi 7600 machine. Detailed information about blood collection and test are presented in appendix 1.

Information about sex, age, income per capita, and educational level (Primary, middle and high) can be found in previous study, total energy intake, smoking status (0= currently not smoking, 1= currently smoking) and drinking status (0= did not drink in the past year, 1= drank alcohol in the past year), and residential area (urban and rural, north and south) were collected as co-variables. Physical activity was defined according to occupation (1=light physical activity, working in a sitting or standing position, such as office worker, teacher; 2=moderate physical activity, such as student or driver; 3= heavy physical activity, such as farmers, loader, miner), and adjusted in the model as well. Adults were categorized into three age groups according to the World Health Organization (WHO) standards (young: ≥18 \& ≤45; adult: >45 \& ≤60; old: >60).

Statistical analysis First, in order to investigate whether the association between dietary diversity and MetS varies between men and women, an interaction term between sex and DDS was added into the multivariable linear model. Accordingly, the descriptive statistical were presented at each tertiles of DDS for each sex(1st, ≤3; 2nd, 4; 3rd, 5-6). Continuous variates are presented as means ± standard deviations and categorical variables are presented as percentages.

The association between DDS and MetS and its individual components was detected by multivariable-adjusted logistic regression, both the OR values and 95% CI were presented. Sex, income per capita, educational level, physical activity, age and age squared, smoking and drinking status, fat share, total energy intake, and residential location were adjusted in the multivariable regression model. Income and total energy intake were measured in logarithm. The association between predicted probability of MetS and age was mapped using kernel weighted local polynomial smoothing; both the fitted probability and 95% confidence interval were presented. The linear trend of odds ratio for DDS was tested by taking DDS as continuous variable in the multivariable logistic regression.

All analyses were performed with Stata multi processing (MP) 13.0 (Stata Crop, USA) and P<0.05 was considered as statistical significance.