The Difference of Being Overweight or Obese between the North and South of China

The Difference of Being Overweight or Obese between the North and South of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research The Difference of Being Overweight or Obese between the North and South of China Daisheng Tang, Tao Bu, Yahong Liu, Xuefan Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-46188/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: The geographical environment, dietary culture, food patterns, and obesity rates are substantially different between the North and South of China. Determining the geographical distribution and local dietary patterns involved in being overweight or obese is useful for designing intervention strategies. Methods: Residents between 18 and 65 years old (n=10,863) from 11 Chinese provinces (five Northern provinces and six Southern provinces) were selected to compare dietary patterns, BMI, and health-related information from the China Health and Nutrition Survey packages in 2011. Linear and logistic regression analyses were performed to assess the strength of the association among geographic variables, the obesity problem, and dietary patterns. Results: The overall prevalence of being overweight or obese was 10.51% higher in the North than in the South. Northern dietary patterns feature a high intake of wheat and soybeans, whereas Southern dietary patterns feature a high intake of rice, vegetables, meat, and poultry. The estimated coefficient of regional variables surrounding dietary score is 1.494; surrounding the odds ratio for being overweight is 1.681, whereas surrounding the odds ratio for obesity is 2.035. Multivariate logistic regression including both the variable of South–North areas and Northern dietary patterns showed a significant correlation with being overweight or obese. Conclusion: Northern areas and their local dietary patterns are more likely to contribute to being overweight or obese. These findings provide support for tracking the progression of obesity, epidemics, and policies that target the ‘‘obesogenic’’ environment, promoting opportunities for persons to access healthy dietary patterns and nutritional balance. Health Policy Overweight Obesity Geographical distribution Dietary patterns South-North division of China Figures Figure 1 Figure 2 Figure 3 Introduction Being overweight or obese is an increasingly serious public health problem affecting every country. This problem is not only associated with developed countries, but has also begun to emerge in the developing world[ 1 ], especially in densely populated China. From 1991 to 2011, the percentage of overweight adults rose from 20.5–42.3%, and obesity increased from 3.75–11.3%[ 2 ]. It is essential to tackle this issue, as being overweight or obese has long-term influences on the development of diseases, including diabetes and hypertensive disease[ 3 ]. In China, it is not only the obesity phenomenon that is a growing problem; there is also the issue that Northerners have a higher prevalence of being overweight or obese than Southerners[ 4 – 6 ]. It is generally believed that the causes of obesity are the socio-cultural environment and lifestyle[ 7 ]. However, no data are available regarding a reduction in the prevalence of the problem from the perspective of dietary culture and regional differences, which is required information for drawing up purpose-designed policies for public health intervention. In china, the Qinling–Huaihe Line is widely recognized to divide China geographically into two regions (the geographical boundaries between the North and the South of China are displayed in Fig. 1 , with the edges of certain parts of the Chinese territory not fully shown on the map) in which there are major differences in terms of the geography and environment[ 8 ]. Because of the geographical environment, residents’ have fostered a diverse and rich dietary culture throughout their long historical activity, and developed some differences of culture and diet between the North and South of China[ 9 ]. Dietary patterns represent a broader prospective on the interactions between nutrients and food intake, indicating that certain patterns are high predictors of risks[ 10 , 11 ]. Previous studies have demonstrated the relationship between dietary patterns and obesity[ 11 , 12 ]. However, to our knowledge, very few data are available concerning geographical environment and dietary culture in terms to dietary patterns and nutrient intake research in epidemic. In addition, no research has explored the impact of geographical differences and dietary culture on the associations between dietary patterns and being overweight or obese in China. Determining the rates of being overweight or obese within regions and dietary patterns is very useful for tracking the progression of the epidemic and establishing the most effective places to target intervention strategies. The goal of the present study is to study the differences in rates of being overweight or obese based on the geographically influenced dietary cultures in North and South China. It is further intended to explore the causes of these differences, identifying the factors associated with geography and dietary patterns. Finally, we hypothesize that geography and dietary pattern differences are the main reasons for being overweight or obese, and we hope to identify more appropriate dietary patterns and lifestyle options to address the growing prevalence of this problem. Materials And Methods Data The data used in this study were taken from the Chinese Health and Nutrition Survey (CHNS) for 2011. The CHNS is an ongoing, open-cohort, international, collaborative project between the University of North Carolina and the Chinese Centre for Disease Control and Prevention, and is also the only longitudinal, household-health-based survey in China[ 13 ]. The purpose of the CHNS is to investigate the impact of the Chinese government’s policies on family planning, health, nutrition, and implemented programs. It also provides information on health and nutritional status during the great Chinese economic transformation. These data mainly measure the impact of health behaviors, nutritional status, and outcomes, as well as changes in demographic, family, individual, economic, and social factors. The present study used a multistage random cluster to sample 10,863 residents (North = 4,411; South = 6,452) aged 18 to 65 years old when other samples with incomplete information were excluded. The regional variables included eleven provinces and were selected for this paper (Fig. 1 ): in the North, this included Heilongjiang, Liaoning, Beijing, Shandong, and Henan; in the South, this included Jiangsu, Shanghai, Hubei, Chongqing, Hunan, Guizhou, and Guangxi. A small section of Henan lies south of the Qinling–Huaihe Line, whereas Jiangsu and Hubei have minor regions located north of the Qinling–Huaihe Line; however, we still consider Henan a Northern province and Jiangsu and Hubei Southern provinces because the Qinling–Huaihe Line is not divided by the provincial boundaries, but by rivers and mountains. Outcome variables Measurements were taken of height (m), weight (kg), and BMI (weight/height 2 ) by trained health workers following standardized protocols from the WHO. The clinical classification criteria indicated that being overweight is defined as a BMI ≥ 24 kg/m² and obesity is defined as a BMI ≥ 28 kg/m²[ 14 ]. Dietary patterns ware presented by dietary score. Individual food consumption was recorded through a 24-hour recall method for three consecutive days for each participant. The eating times and weight of each type of food the participants consumed over the course of three days were recorded to determine the total average consumption of each category of food per day. The dietary scores ranking the reasonable intake levels of food patterns were obtained through the Dietary Balance Index (DBI)[ 15 , 16 ], which is a comprehensive statistical technique for assessing under- and over-nutrition in Chinese residents, thus providing a useful tool to monitor dietary healthiness[ 16 ]. The DBI method is similar to the Healthy Eating Index[ 17 ] and the Diet Quality Index[ 18 ]. The closer the dietary score is to zero, the more reasonable the dietary intake is. In contrast, the higher the dietary score is, the more likely the dietary intake equates to over-nutrition. Scoring details of the DBI method can be found in Table S1. Independent variables The independent variables in this study is dietary quality score and regional variables, dietary score is not only the dependent variable for former stage but also the independent variable for latter stage. The control variables were household income, education level, age, sedentary time, and some health-related information about parents collected via in-home interviews and general information questionnaires. The sample’s education level was divided into five levels (none, primary school, lower middle school, upper middle school, and university). Sedentary time includes all the sedentary activities taking place at home, such as watching TV, playing games, surfing the Internet, and reading. This study did not directly consider the variable of “average number of times per week of engaging in physical or outdoor activity” because individual exercise patterns were inconsistent. As a complement, the sedentary time variable (minutes per week) was used to control for the effect of physical activity on obesity analysis. In addition, individual registration, smoking and alcohol drinking are also considered in our present study. Participants were selected in the 18-to-65 age range to eliminate the effects of the changing dietary patterns in children or the elderly. In addition, we also considered how the family environment factors into children’s obesity and nutritional balance, such as parental dietary patterns, obesity, hypertension, and diabetes[ 19 ]. In this study, regional variables were defined in three different forms by the span of geographic space: regional type I, regional type II, and regional type III. Regional type I is divided into 11 areas according to the division of administrative provinces in China (the region sequentially moves from the southernmost Guangxi province to the northernmost Heilongjiang province). Regional type II is divided into three areas: North (Heilongjiang, Liaoning, Beijing, and Shandong), Central (Henan, Jiangsu, and Hubei) and South (Shanghai, Chongqing, Hunan, Guizhou, and Guangxi). Regional type III is divided into two areas: The South and the North. From regional type I to regional type III, the span of geographical space gradually increases. Extraction of dietary patterns Dietary patterns were extracted using principal component factor analysis based on 12 main categories of food[ 20 ]. Two dietary patterns were obtained from participants’ food consumption records with eigenvalues (> 1), and the variance explained by each factor. The sign defining these linear combinations, called factor loadings, determined the correlations of each food category with the factor. Food categories with loadings on a factor were used to describe the characteristics of dietary patterns; higher loadings mean the categories share more variance with the factor. Labelling of the factors is mainly descriptive and based on our explanation of dietary pattern structures, and participants were labelled according to dietary-pattern-specific factor scores. Scores for each dietary pattern were counted as the sum of the factor loading coefficients and standardized daily consumption of each food category associated with the corresponding pattern. In this study, we used two kinds of scores for diet: one was calculated via the DBI, and the other was obtained using factor analysis. The DBI method can comprehensively show the quality of food intake, which mainly scores the level of food intake according to the level of each food (found in Table S1), and finally obtain the dietary score through a summation. In contrast, the factor analysis method mainly indicates the characteristics of specific dietary patterns, scoring points by the weighted sum of the combination coefficients of a certain pattern. Therefore, we have mainly employed dietary score via the DBI method to compare and study the food intake of residents between the North and South, but using the score calculated via factor analysis to present the characteristics of the two newly extracted Southern and Northern dietary patterns, in the final regression analysis (Table 6 ). Statistical study Related data were analyzed via Stata version 14 (Stata Corp, College Station, TX, USA) to determine the causes of the different rates of being overweight or obese between China’s North and South. First, a series of descriptive analyses were conducted regarding the participants’ BMI and demographic characteristics (e.g., education level, household income, age), rates of being overweight or obese, the degree of different categories of food intake, and parental health-related conditions (e.g., BMI, hypertension, diabetes) and food intake. The DBI method was also applied as a dietary score measure to assess the quality of dietary intake, in which a high score means more food intake and over-nutrition being more likely. Comparisons of the changes in prevalence across regional type III were made using t-tests for continuous variables and χ 2 tests for binary variables (allowing for unequal variance between two groups). Second, we assessed correlations between various categories of food intake and regional type III, and hoped to extract two kinds of dietary patterns using factor analysis. Furthermore, we carried out a series of regression analyses to assess how regions have influenced dietary scores and being overweight or obese, adding certain control variables related to parental obesity-related information. Finally, on the basis of the existing relationships between regional variables and dietary scores, we further took being overweight or obese as a dependent variable to conduct multivariate logistic regression with the two dietary patterns extracted using factor analysis and the South–North regional dummy to verify that these relationships with different dietary patterns and geographical variations have a significant impact on rates of being overweight or obese. Results Significant increases in mean BMI were observed when moving from the more southern to the more northern areas (Table 1 ). The BMI in the North was 23.732%, whereas in the South, it was 22.694% (p < 0.001). Household income, education level, and sedentary time also exhibited statistical significance (p < 0.05), which was lower than the significance level for BMI. In addition, both Southern parents’ BMI levels were higher than those of parents living in the North (see Table 1 ; all p ≤ 0.001 for BMI), and a portion of the parents’ obesity-related diseases also showed significance between the two regions. These differences in BMI for both participants and their parents demonstrated that there was a highly significant difference in rates of being overweight or obese between the South and North of China. Table 1 Samples’ BMI and demographic characteristics along with their parents’ health-related information Items Number P value North South BMI 23.732 22.694 0.000 Income (yuan) 15043 15266 0.028 Educated level 3.071 2.823 0.021 Age 31.469 33.096 0.114 Sedentary time (minutes) 403.923 453.139 0.001 Mother’s BMI 25.170 23.593 0.000 Father’s BMI 25.204 23.327 0.000 Items Percentage P value Percentage of mother’s hypertension 0.254 0.205 0.154 Percentage of mother’s diabetes 0.075 0.055 0.040 Percentage of father’s hypertension 0.214 0.252 0.388 Percentage of father’s diabetes 0.074 0.074 0.006 Furthermore, we calculated the rates of being overweight or obese in 11 provinces. From the Northern areas to the Southern areas, the prevalence shows a roughly decreasing trend (Fig. 2 ). In addition, the average rate of being overweight including obese in the North was higher than that in the South by 10.51%. Figure 2 portrays how there are significant differences in the rates of being overweight including obese between the North and South of China. As there are significant differences in the rates of being overweight including obese between the North and South, we further verified whether there is a difference in dietary patterns. Analyzing the statistical description of food intake and dietary scores, we observed that most categories of food feature significant differences between the two areas (Fig. 3 ). Intake of rice, vegetables, poultry, meat, fish, and alcohol in the South was greater than that in the North, and statistical significance was reached for all categories except poultry and alcohol (p < 0.1). In contrast, intake of wheat, soybeans, fruits, tubers, eggs, and dairy in the North was higher than that in the South, and statistical significance was reached except for soybeans (p < 0.1). In addition, rice and wheat were the largest difference in food categories between the North and South; the average consumption of rice in the South was 165.9 g higher than that in the North, whereas the average consumption of wheat in the North was 158.5 g higher than that in the South. Furthermore, there were also significant differences in dietary scores between the North and South. The dietary scores of Northerners were higher by 1.1 points than those of Southerners on average, which means that the total quality of food intake in the North is more likely to reflect over-nutrition than in the South. In conclusion, there are significant differences in dietary patterns among residents between the North and South. In addition, we found that most categories of food were highly correlated with regional variables from the South to the North (Table 2 ). Wheat, soybeans, fruits, tubers, eggs, and dairy had significantly positive correlations with regional variables, whereas rice, vegetables, poultry, meat, and fish had significantly negative correlations with regions, demonstrating that there were significant differences with respect to geographical variation in the dietary patterns of Chinese residents. In addition, the estimated coefficient of wheat and rice was the largest but opposite, which reveals that the difference between wheat and rice was the most significant between the two areas, and such results were consistent with Song’s findings[ 9 ]. Table 2 Correlation analysis between different categories of food with regions Items Wheat Soybean Fruits Tuber Egg Dairy Regional type III 0.451*** 0.049* 0.090*** 0.170*** 0.229*** 0.107*** Items Rice Vegetables Poultry Meat Fish Alcohol Regional type III -0.364*** -0.210*** -0.119*** -0.292*** -0.115*** -0.007 *** p < 0.01, ** p < 0.05, * p < 0.1 Furthermore, we extracted two types of dietary patterns through factors analysis, and they were defined as Southern dietary patterns and Northern dietary patterns respectively based on regional characteristics of food consumption. Southern dietary patterns were rich in rice, vegetables, meat, poultry, etc.; Northern dietary patterns were abundant in wheat, soybeans, etc. The attribution of each category of food was based on the rotation component matrix from the factor analysis (Table 3). Finally, we obtained factor scores of the two types of dietary patterns using the sum of the factor loading coefficients and standardized daily consumption of each food category, and we defined them as new variables for Southern dietary patterns and Northern dietary patterns, which is generally consistent with the actual information surrounding the consumption of various types of food in the two regions[ 9 ]. Table 3. Weighting combinations of the two dietary patterns extracted from factor analysis Items Southern dietary patterns Northern dietary patterns Rice 0.175 -0.308 Vegetables 0.421 0.063 Meat 0.337 -0.052 Poultry 0.348 0.117 Wheat 0.013 0.558 Soybean 0.178 0.507 Fruits 0.072 0.141 Tuber 0.128 -0.013 Dairy -0.024 -0.090 Fish 0.197 -0.103 Egg -0.037 0.052 Alcohol 0.178 0.046 Snack 0.052 -0.015 According to statistics, there are indeed significant differences between the North and South of China with respect to dietary patterns and rates of being overweight or obese. Therefore, we further assessed the causes of these differences through empirical research. All of these estimated coefficients for regional variables had a significant correlation (p < 0.01) with dietary score (Table 4 ), and this gradually increased from 0.287 to 1.494 with a rise in geographic span (from model 1 to model 3). In model 1, the geographic span gradually changed from South to North according to adjacent provinces for all 11 provinces. Furthermore, in model 2, the geographic span was first changing from the Southern to Central areas, and then to the Northern areas, and the size of the geographic span was larger than regional type I. Finally, in model 3, the geographic span changed directly from South to North, adjusted for the three Central provinces (Henan, Jiangsu, and Hubei) into Northern and Southern areas mainly according to their geographical locations, and the geographic span of regional types III were the greatest than that of regional types I and II. In conclusion, the larger the geographic span, the greater the difference in dietary scores. Furthermore, the dietary score rose from the South to the North, indicating that Southerners’ dietary patterns were less prone to over-nutrition than those of Northerners. After adjusting for income, age, education level, sedentary time, registration, smoking, drinking and parents’ dietary scores, the estimated coefficient of the regions was still significant (p < 0.1). Moreover, education level and sedentary time were significantly negatively correlated with dietary scores, which was consistent with Woo’s results[ 21 ]. The samples’ dietary scores were also significantly correlated with those of their parents, which may be because there is a similar dietary difference in generations between the North and South[ 22 ]. Table 4 Linear regression analysis for regions with respect to dietary scores Items Model 1 Model 2 Model 3 Model 4 Regional type I 0.287*** Regional type II 1.076*** Regional type III 1.494*** 0.384** Income 0.215** Age 0.055*** Educated level -0.169** Sedentary time -0.0003* Mother’s dietary scores 0.633*** Father’s dietary scores 0.207*** Yes or no Rural 0.137 Smoking 0.264 Alcohol drinking 0.150 Constant 6.201*** 2.277*** 3.693*** -0.341* *** p < 0.01, ** p < 0.05, * p < 0.1 Note : Model 1, model 2 and model 3 only considered the different sizes of geographic span to conduct regression analysis of diet scores. Model 4 included all of model 3 and was further adjusted for household income, age, education level, sedentary time and parents’ dietary scores. It should be noted that a large number of samples are lost in the regression model of 4 (n = 1,515 residents (North = 565; South = 986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult. Furthermore, we evaluated the causes of being overweight or obese regarding rate differences through regional factors. All of the odds ratios (OR) for regional variables exhibited a significant correlation (p < 0.01) with being overweight or obese (Table 5 ); the OR for being overweight gradually increased from 0.923 to 1.681, and the OR for obesity gradually rose from 0.902 to 2.035 with the increase in geographic span (from model 5 to model 7 and from model 9 to model 11). The variation trend of the OR coefficient was consistent, as per Table 4 . After adjusting for income, age, education level, sedentary time, and parents’ obesity-related information, the regional OR coefficients were still of significance. In conclusion, the possibility of becoming overweight or obese increases gradually from the Southern areas to the Northern areas, and the greater the geographic span, the faster these rates increase. Furthermore, the samples’ rates of being overweight or obese were also significantly correlated with those of their parents’ obesity-related history. Table 5 Multivariate adjusted odds ratios for regions with respect to being overweight or obesity Items Overweight Obesity Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Regional type I 0.923*** 0.902*** Regional type II 1.345*** 1.482*** Regional type III 1.681*** 1.319* 2.035*** 1.541* Income 1.159 1.341* Age 1.038*** 1.038** Educated level 0.978 0.976 Sedentary time 1.000** 1.000 Yes or no Rural 1.047 1.360 Smoking 1.061 0.630 Alcohol drinking 1.359* 1.498 Mother’s obesity 1.497* 2.048** Mother’s hypertension 1.778*** 0.941 Mother’s diabetes 1.735* 1.196 Father’s obesity 2.150*** 3.007*** Father’s hypertension 1.235 1.164 Father’s diabetes 1.04 0.831 Constant 1.403*** 0.473*** 0.670*** 0.030*** 0.263*** 0.064*** 0.100*** 0.001*** *** p < 0.01, ** p < 0.05, * p < 0.1 Note : Models 5 to 7 and models 9 to 11 only considered the different sizes of the geographic span for being overweight and obesity, respectively. Model 8 and model 12 includes all of model 7 and model 11 respectively, and further adjusts for control variables household income, age, education level, sedentary time and parents’ histories of obesity and chronic health-related information. It should be noted that a large number of samples are lost in the regression model of 8 and 12 (n = 1,515 residents (North = 565; South = 986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult. According to the aforementioned studies, there are significant differences in dietary patterns and rates of being overweight or obese for residents of the two regions. Southern rates of being overweight or obese and dietary scores were lower than those of the North. Therefore, we further verified the influence of both dietary differences and geographical differences in terms of the problem of residents being overweight or suffering from obesity. In Table 6 , we show the two new dietary patterns extracted using factor analysis, which can approximately represent the characteristics of Northern and Southern dietary patterns rather than artificially dividing two local dietary patterns by geographical boundaries. Table 6 Multivariate adjusted odds ratios for dietary patterns and regions with respect to being underweight or suffering from obesity Items Overweight Obesity Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20 Regional type III 1.464*** 1.697** Northern dietary patterns 1.226*** 1.331*** 1.223*** 1.258*** 1.333*** 1.199* Southern dietary patterns 0.996 1.167** 1.242*** 0.875*** 0.984 1.083 Income 1.061 1.058 1.099 1.095 Age 1.038*** 1.039*** 1.018* 1.018* Educated level 0.968 0.960 0.954 0.941 Sedentary time 1.000** 1.000** 1.000 1.000 Yes or no Rural 0.839 0.833 0.868 0.857 Smoking 0.953 0.941 0.848 0.838 Alcohol drinking 1.272* 1.282** 1.253 1.263 Mother’s obesity 1.820*** 1.758*** 2.218*** 2.116*** Mother’s hypertension 1.374** 1.364** 1.216 1.207 Mother’s diabetes 1.334 1.305 0.894 0.888 Father’s obesity 2.241*** 2.099*** 2.987*** 2.746*** Father’s hypertension 1.095 1.113 1.057 1.080 Father’s diabetes 1.094 1.089 0.902 0.872 Constant 0.780*** 0.781*** 0.096*** 0.088*** 0.132*** 0.134*** 0.026*** 0.023*** *** p < 0.01, ** p < 0.05, * p < 0.1 Note : Models 13 to 14 and models 17 to 18 only considered the different dietary patterns for being overweight and obesity, respectively. Models 15 and 19 feature logistic regression analysis with both new two dietary patterns. Model 16 and 20 includes all of model 15 and 19 respectively, and further adjusts for household income, age, education level, sedentary time, parents’ history of obesity and chronic health-related information for regional type III. It should be noted that a large number of samples are lost in the regression model of 15, 16 ,19 and 20 (n = 1,515 residents (North = 565; South = 986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult. Northern dietary patterns exhibited a significant OR for being overweight of 1.226 and for being obese of 1.258. Southern dietary patterns only showed a significant effect for being obese of 0.875. Moreover, after adjusting for income, age, education level, sedentary time, and parents’ history of obesity and chronic health-related information, the OR coefficient for Northern dietary patterns was still significant for being overweight or obese. Finally, we allotted the new two dietary patterns and regional type III to models 16 and 20, with the results showing that the OR coefficient of both regions and Northern dietary patterns exhibited a significant correlation with being overweight or obese, but Southern dietary patterns only had a significant coefficient for being overweight, demonstrating that Northern dietary patterns is more likely to cause obesity. The estimated coefficient of Northern dietary patterns was larger than that of Southern dietary patterns for being overweight, and Southern dietary patterns only had a significant effect on being overweight. All of these phenomena and estimated coefficients indicate that Northern dietary patterns have a greater and stable influence on being overweight or suffering from obesity. These phenomena also suggest that geographical differences not only directly affect the prevalence of being overweight or obese, but also indirectly impact the prevalence of being overweight or obese through influencing dietary patterns between the two areas. Discussion Being overweight or obese is one of the most important social and health problems, and its prevalence is increasing continuously worldwide, including in China[ 4 , 23 , 24 ]. Currently, it is generally believed that lifestyle and diet are the primary causes of being overweight or suffering from obesity[ 6 , 25 ], and that the future solution for such epidemiological issues should come from political policies based on prevention of those aspects related to health and social customs surrounding being overweight or obese. However, not enough is yet known about these factors, which furthermore may vary from place to place[ 7 , 26 ]. In this study, we systematically analyzed the differences in dietary patterns and the prevalence of being overweight or obese between the South and North of China. The prevalence of being overweight or obese in the South was 10.51% less than that of the North, with this prevalence falling when moving through regions from Northern areas to Southern areas. Moreover, the Southerners’ dietary score regarding the quality of food consumed was also lower by 1.1% than that of Northerners. People in the North have a high intake of wheat, tubers, fruits, etc., whereas individuals in the South eat more rice, vegetables, meat, poultry, etc. From the South to the North, all of these estimated coefficients regarding dietary patterns and being overweight or obese were positively and significantly correlated with the geographical variable. In addition, with the spatial span increase defined by three regional types from the South to the North (neighboring provinces, South–Central–North areas, and South–North areas), the estimated coefficients for dietary score increased from 0.287 to 1.494, the OR for being overweight rose from 0.923 to 1.681, and the OR for obesity increased from 0.902 to 2.035. Finally, after placing regional variables and the two dietary patterns extracted using factor analysis into the same model, the OR of the three variables for being overweight were statistically significant at 1.464, 1.223 and 1.242, respectively, but only the OR of Northern dietary patterns and regional type III for obesity were statistically significant at 1.697 and 1.199, respectively. In short, residents from the South were also less likely to be overweight or obese than those from the North before and after adjustment for individual socio-demographic characteristics and household factors[ 5 ]. Firstly, there are significant differences in dietary patterns between the North and South of China. One plausible contributor is regional differences in the geographical environment. China has a natural, geographical dividing line—the Qinling–Huaihe Line, which consists of the Qinling Mountains and Huaihe River—which leads to differences between the North and South in many respects[ 8 ]. This natural dividing line is also a dividing line in terms of climate. In the North, there is a temperate monsoon climate with little rainfall. In contrast, in the South, there is subtropical monsoon climate with plenty of rain[ 27 ]. Owing to the difference in climate between the North and South, the residents’ farming methods are also different. Residents in the North mainly cultivate drought-tolerant crops, such as wheat, whereas residents in the South primarily grow subtropical cash crops, including rice and tea[ 28 ]. As a result, over the course of history, residents have established a variety of dietary cultures with local geographic characteristics, and the types of local agricultural products available may be responsible for some of the differences in dietary patterns[ 29 ]. For example, wheat became a main staple food in the North, and rice a major staple in the South. The present study’s findings are consistent with the actual geographical and cultural characteristics of China as well as findings of previous works[ 9 ]. Furthermore, we applied factor analysis to extract two dietary patterns that roughly represent the difference in dietary characteristics between the North and South; these extracted dietary patterns also show that Southern dietary patterns are rich in rice, vegetables, meat, and poultry, whereas Northern dietary patterns are rich in wheat, soybeans, etc. A number of studies have established that the “modern high-wheat” pattern is rich in fat and induces a high risk of obesity and metabolic syndromes, whereas “vegetable-rich” and “healthy patterns” have had no associations with metabolic syndromes[ 10 , 30 ]. Zhang’s work showed that traditional Southern dietary patterns feature a high intake of vegetables, fish, and meat, or what constitutes a balanced dietary pattern that may help to reduce the risk of chronic diseases, such as abdominal obesity and hypertension[ 31 ]. In addition, climate conditions may contribute to the differences in nutritional intake. The North is colder than the South, and as such, Northerners need to take in more energy to resist the cold, so their total food consumption is more than that in the South[ 32 ]. Meanwhile, Southern residents usually eat coarse fiber foods, such as corn and buckwheat, which can easily enhance a sense of satiety and control the amount of energy consumed[ 9 , 33 ]. As a result, the differences in dietary patterns may be contributed to the geographical differences in climate and culture. Secondly, the odds of being overweight or obese change rapidly and with positive significance as the geographic variables moving from South to North. When the region variable sequentially moves from one province to another across all 11 provinces from the Southern areas to the Northern areas, the prevalence of being overweight or obese slowly rises, but it increases quite quickly when the region variable directly moves from the Southern to the Northern areas. These phenomena and positive estimated coefficients indicated that the prevalence of being overweight or obese is significantly correlation with regional variables. This may also be explained by differences in physical activity[ 6 , 34 , 35 ]. The difference between energy intake and output partial contributed by physical activity means that rates of being overweight or obese are higher in the North than in the South[ 5 ]. Another reasonable contributing factor may be dietary patterns. The present findings indicate that Chinese residents established local characteristics of lifestyle and diet cultures throughout long-term historical activities because of the existence of a boundary line between the North and South, which further affects residents’ dietary patterns. Overall, differences in residents’ rates of being overweight or obese may derive from characteristics of the long-established culture or lifestyle as impacted by an individual’s residence, features of the dietary culture, and the regions themselves; persons in the North have greater eating scores and are more overweight or obese than those in the South. Swinburn and Egger have outlined an ecological framework for understanding rates of being overweight or obese, and they distinguished specific types of environments, including physical, economic, and socio-cultural environments[ 36 , 37 ]. Hawkins and Griffiths found that the United States’s geography affects early childhood rates of being overweight through dependence after adjustment for individual socio-demographic characteristics[ 38 ]. Angela also demonstrated that pre-adolescent children in Germany are considered overweight with marked geographical differences in prevalence[ 39 ]. Regions with unique environmental, cultural, or policy-related (e.g., infrastructure) traits may provide or support opportunities for physical activity and/or access to a healthy diet, which have different influences on the prevalence of being overweight or obese. Based on the geographical differences and dietary habits formed throughout its long history, there are certain dietary and cultural differences between Northern and Southern China. However, the differences may not only be seen in contemporary residents, but also their elders, and the present study determined that Northern elderly residents have higher rates of being overweight or obese and dietary scores than those of Southern elders. The rates of fathers and mothers being overweight or obese in the North are 19.66% and 18.81% higher than those in the South, respectively, and the dietary scores of those in the North are 1.72 and 0.88 points higher than those in the South, respectively. Furthermore, parents’ rates of obesity had a positive and significant correlation with participants’ rates of being overweight or obese, and parents’ dietary scores were also significantly correlated with participants’ dietary scores. This difference and this correlation indicate that geographical differences may have a significant impact on dietary patterns for each generation, and local dietary patterns and geographical differences further affect residents’ nutritional balance[ 40 , 41 ]. Furthermore, residents’ household income, age, education level, and sedentary time also exerted some influence on the prevalence of being overweight or obese. Persons with higher household income, age, and sedentary time were more likely to be obese, but those with higher education levels were less prone to obesity[ 42 – 44 ]. Currently, with the rapid increasingly economy and concomitant changes in lifestyle, there has been a markedly high intake of fast foods in China[ 45 ]. However, because most fast foods have high fat and energy levels, thus leading a greater risk of hypertension, glucose, and being overweight or obese[ 46 ]. It is necessary to be aware of the problems of rapidly changing dietary patterns and obesity-related diseases. The present research has several strengths compared to other studies. We selected 11 representative provinces to demarcate the differences in dietary patterns and rates of being overweight or obese between the North and South of China, and for the first time, we studied the causes of these differences in an empirical manner considering geographical differences and dietary patterns, specifically taking into account traditional Chinese culture[ 9 , 47 ]. In addition, we comprehensively described specific dietary patterns of residents from the North and South while comparing the differences in the dietary structure of residents by the method of DBI, which could serve as a robust predictor of being overweight or obese and lead to effective policy interventions. Furthermore, the study provides information regarding the relationships among the South–North geographical difference, dietary patterns, and the prevalence of being overweight or obese. Northerners in the study ate differently from Southerners, at least from a qualitative perspective, probably consuming more food and leading to more opportunities for obesity. Finally, based on the natural geographical dividing lines in China, we discussed the historical origin of the differences in residents’ dietary patterns and the causes of the differences in rates of being overweight or obese, combining the characteristics of an individual’s living environment and traditional culture. Our results also suggest that a series of relevant policies should be put in place to support the improvement of dietary patterns and living habits, thereby removing barriers to healthy eating and nutritional balance. Several limitations of this study did exist. We did not specifically evaluate the factors of economic status, family environment, physical activity, education level, registration, smoking, drinking, or genetic factors, and simply accounted for them as control variables in this paper. Further work is necessary to consider differences in the surrounding environment between the North and South to determine how these differences may promote a healthier nutritional balance. In addition, because of the limited information on nutrition, we did not assess the differences in nutritional composition between Northerners and Southerners. Certainly, there are other factors that may cause differences in the problems of being overweight or obese. Conclusions There are indeed significant differences in dietary patterns and obesity issues between the North and South of China. Northern dietary scores and the rates of being overweight or obese were higher than those of Southern China. Our findings suggest that China’s natural geographical difference between the North and South may lead to different dietary patterns starting with local characteristics established throughout a long-term history, then geographical differences and formed local dietary patterns eventually resulting in different rates of being overweight or obese between the two regions. This provides empirical evidence for policies that target the ‘‘obesogenic’’ environment and promote opportunities for persons to access healthy dietary patterns and nutritional balance. Abbreviations BMI = body mass index; OR = odds ratio; DBI = dietary balance index; Chinese Health and Nutrition Survey Declarations Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Additionally, this article also obtained the ethics approval by the ethical review board of Beijing Jiaotong University. All authors agreed to participate and publicise related study in the paper, and informed consent was obtained from all individual participants included in the study. Consent for publication Not required Availability of data and materials The dataset supporting the conclusions of this article is available from the China Health and Nutrition Survey (CHNS), and the datasets used and/or analyzed during the current study are available from the corresponding author on request. Competing interests The authors declare that there is no potential conflict of interest. Funding This work was supported by a grant-in-aid for Chinese National Funding of Social Sciences (Grant 18ZDA086) and a grant-in-aid for Key Program of Beijing Social Science Foundation (Grant 17GLA006). This funding body did not play any role in the design of the study and in the writing the manuscript but in the collection of data and charges providing. Authors’ contributions All authors contributed to this work. Daisheng Tang and Xuefan Dong worked on the study design and method. Tao Bu was respondsible for manuscript design, data analysis and paper writing while Daisheng Tang and Xuefan Dong supervised analysis again. Yahong Liu provide interpretive input and words correcting. All authors critically reviewed and approved the final manuscript. Acknowledgements Thank for Chinese Center for Disease Control and Prevention to provide China Health and Nutrition Survey (CHNS) data supporting. Thanks to all the subjects involved in the experiment, which agree to free use the data free and to publish relevant data or reports. References Poobalan, A. and L. Aucott, Obesity Among Young Adults in Developing Countries: A Systematic Overview. Curr Obes Rep, 2016. 5(1): p. 2-13. Mi, Y. J., Zhang, B., Wang, H. J., Yan, J., Han, W., Zhao, J., Tian, Q. B. (2015). Prevalence and Secular Trends in Obesity Among Chinese Adults, 1991-2011. American Journal of Preventive Medicine, 49(5), 661-669 Outbreaks, D. and O. Complications, Obesity epidemic puts millions at risk from related diseases. Israel Journal of Medical Sciences, 1997. 33(10). Wildman, R.P., et al., Trends in overweight and obesity in Chinese adults: between 1991 and 1999-2000. Obesity, 2012. 16(6): p. 1448-1453. Yu, Z., et al., Obesity related metabolic abnormalities: Distribution and geographic differences among middle-aged and older Chinese populations. Preventive Medicine, 2009. 48(3): p. 272-278. Gu, D., et al., Prevalence of the metabolic syndrome and overweight among adults in China. Lancet, 2005. 365(9468): p. 1398-1405. Hill, J.O. and J.C. Peters, Environmental contributions to the obesity epidemic. Science, 1998. 280(5368): p. 1371-1374. He, K., et al., Overview of karst geo-environments and karst water resources in north and south China. Environmental Earth Sciences, 2011. 64(7): p. 1865-1873. Song, F. and M.S. Cho, Geography of Food Consumption Patterns between South and North China. Foods, 2017. 6(5). Xianwen, S., et al., Dietary pattern and its association with the prevalence of obesity and related cardiometabolic risk factors among Chinese children. Plos One, 2012. 7(8): p. e43183-e43183. Jing, S., B. Nicholas and H. Andrew, Dietary Pattern and Its Association with the Prevalence of Obesity, Hypertension and Other Cardiovascular Risk Factors among Chinese Older Adults. International Journal of Environmental Research & Public Health, 2014. 11(4): p. 3956-3971. Kuhnlein, H.V., et al., Arctic Indigenous Peoples Experience the Nutrition Transition with Changing Dietary Patterns and Obesity. Journal of Nutrition, 2004. 134(6): p. 1447-1453. Zhang, B., et al., The China Health and Nutrition Survey, 1989-2011. Obesity Reviews, 2013. 15(S1): p. 2-7. Eveleth and B. Phyllis, Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. Geneva Switzerland Who, 1995. 854(6): p. 1-452. Xiaoyue, X., et al., Assessing dietary quality of older Chinese people using the Chinese Diet Balance Index (DBI). Plos One, 2015. 10(3): p. e0121618. Zhang, Q., et al., Dietary Balance Index-07 and the Risk of Anemia in Middle Aged and Elderly People in Southwest China: A Cross Sectional Study. Nutrients, 2018. 10(2): p. 162. Adam, D. and C.D. Rehm, Consumption of low-calorie sweeteners among U.S. adults is associated with higher Healthy Eating Index (HEI 2005) scores and more physical activity. Nutrients, 2014. 6(10): p. 4389-4403. Haines, P.S., A.M. Siegariz and B.M. Popkin, The Diet Quality Index revised: a measurement instrument for populations. Journal of the American Dietetic Association, 1999. 99(6): p. 697-704. Newby, P.K., Are dietary intakes and eating behaviors related to childhood obesity? A comprehensive review of the evidence. Journal of Law Medicine & Ethics, 2010. 35(1): p. 35-60. Batis, C., et al., Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults. Public Health Nutrition, 2016. 19(2): p. 195-203. Woo, J., et al., Influence of educational level and marital status on dietary intake, obesity and other cardiovascular risk factors in a Hong Kong Chinese population. European Journal of Clinical Nutrition, 1999. 53(6): p. 461-467. Stafleu, A., et al., Family resemblance in energy, fat, and cholesterol intake: a study among three generations of women. Preventive Medicine, 1994. 23(4): p. 474. Xu, X., et al., Dietary Pattern Is Associated with Obesity in Older People in China: Data from China Health and Nutrition Survey (CHNS). Nutrients, 2015. 7(9): p. 8170-8188. Jia, P., et al., Time Trend and Demographic and Geographic Disparities in Childhood Obesity Prevalence in China-Evidence from Twenty Years of Longitudinal Data. International Journal of Environmental Research & Public Health, 2017. 14(4): p. 369-. Reynolds, K., et al., Prevalence and Risk Factors of Overweight and Obesity in China*. Obesity, 2012. 15(1): p. 10-18. Sherpa, L. Y., Stigum, H., Chongsuvivatwong, V., Thelle, D. S., & Bjertness, E. (2010). Obesity in Tibetans Aged 30? Living at Different Altitudes under the North and South Faces of Mt. Everest. International Journal of Environmental Research & Public Health, 7(4), 1670-1680. Wang, Z., W. Fang and Y. Liao, Assessment of physical vulnerability to agricultural drought in China. Natural Hazards, 2013. 67(2): p. 645-657. Cheng, C., J. Ren and R. Wang, Spatial-temporal distribution of agricultural eco-efficiency in China. Acta Ecologica Sinica, 2014. 34(1). Gracey, M., Historical, cultural, political, and social influences on dietary patterns and nutrition in Australian Aboriginal children. American Journal of Clinical Nutrition, 2000. 72(5 Suppl): p. 1361S-1367S. Hui, C., et al., A prospective study of dietary patterns and mortality in Chinese women. Epidemiology, 2007. 18(3): p. 393-401. Zhang, J.G., et al., Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women. European Journal of Clinical Nutrition, 2015. 69(9): p. 1009. Aya, Health differences between north and south caused by five reasons[J]. World of labor security, 2016. (13):70-70. [In Chinese]. Jian, Z., C. Wang and J. Gao, Analysis on the dietary lipid pattern in different areas of China. Journal of Hygiene Research, 1999. 28(6): p. 359. Wu, X., et al., Changes in average blood pressure and incidence of high blood pressure 1983-1984 to 1987-1988 in four population cohorts in the People's Republic of China. The PRC-USA Cardiovascular and Cardiopulmonary Epidemiology Research Group. Journal of Hypertension, 2012. 14(11): p. 1267-74. Zhou, B., et al., The relationship between dietary factors and serum lipids in Chinese urban and rural populations of Beijing and Guangzhou. PRC-USA Cardiovascular and Cardiopulmonary Research Group. International Journal of Epidemiology, 1995. 24(3): p. 528. Egger, G. and B. Swinburn, An "ecological" approach to the obesity pandemic. Bmj, 1997. 315(7106): p. 477-480. Swinburn, B., G. Egger and F. Raza, Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 1999. 29(6 Pt 1): p. 563. Hawkins, S., et al., Regional differences in overweight: an effect of people or place? Archives of Disease in Childhood, 2008. 93(5): p. 407-413. Liese, A.D., et al., Burden of overweight in Germany: prevalence differences between former East and West German children. European Journal of Public Health, 2006. 16(5): p. 526. Pineda, E. and J. Mindell, Association of the food environment with obesity: a systematic review of geographical and statistical methods. Lancet, 2016. 388: p. S89. Thornton, L.E., J.R. Pearce and A.M. Kavanagh, Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary. International Journal of Behavioral Nutrition & Physical Activity, 2011. 8(1): p. 71. Chang, V.W. and D.S. Lauderdale, Income disparities in body mass index and obesity in the United States, 1971-2002. Archives of Internal Medicine, 2005. 165(18): p. 2122-2128. Gouda, J. and R.K. Prusty, Overweight and obesity among women by economic stratum in urban India. Journal of Health Population & Nutrition, 2014. 32(1): p. 79-88. Monteiro, C.A., W.L. Conde and B.M. Popkin, Independent effects of income and education on the risk of obesity in the Brazilian adult population. Journal of Nutrition, 2001. 131(3): p. 881S. Cheng, T.O., Effects of fast foods, rising blood pressure and increasing serum cholesterol on cardiovascular disease in china. American Journal of Cardiology, 2006. 97(11): p. 1676-1678. Mohamed, S., Functional foods against metabolic syndrome (obesity, diabetes, hypertension and dyslipidemia) and cardiovascular disease. Trends in Food Science & Technology, 2014. 35(2): p. 114-128. Zhang, Y. X., Wang, Z. X., Zhao, J. S., & Chu, Z. H. (2016). Prevalence of Overweight and Obesity among Children and Adolescents in Shandong, China: Urban and rural Disparity. Journal of Tropical Pediatrics, 62(4), 293 Supplementary Files TableS1.docx TableS1.docx TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-46188","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1078759,"identity":"fa2696a6-447b-4d4b-b61e-c03b27512e63","order_by":0,"name":"Daisheng Tang","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daisheng","middleName":"","lastName":"Tang","suffix":""},{"id":1078760,"identity":"7c81ed0d-674a-421f-8722-50c3aa62695a","order_by":1,"name":"Tao Bu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYBAC+wMMDMwfGGx4+NkbiNTCBsTMEgxpMpI9B0jRwsNw2MbghgOxWtgPH2CWbDvPw3CDgfHDxxxitPCkJTB/bLvNwzi7gVly5jaiHJZjALTlNg+zzAE2Zl6itPC/MWDmbTvHwyaRQKwWiRyQlgM8PCRoeZbALHEumUeC52AzkX7hTz7A/KHMzt7+ePPBDx+J0QIE7D8gNGMDcepHwSgYBaNgFBAGAKNxLWCnPZ9UAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4115-4546","institution":"Beijing Jiaotong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Bu","suffix":""},{"id":1078761,"identity":"9c572ccf-6ac5-4a50-8486-4a91b80b977d","order_by":2,"name":"Yahong Liu","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yahong","middleName":"","lastName":"Liu","suffix":""},{"id":1078762,"identity":"55f12f54-3a83-4255-9c14-dd4c71095a8b","order_by":3,"name":"Xuefan Dong","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuefan","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2020-07-20 12:11:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-46188/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-46188/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1731622,"identity":"76bdf930-8811-45e8-a4d5-ba1647170238","added_by":"auto","created_at":"2020-07-30 15:04:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39406,"visible":true,"origin":"","legend":"Geographical boundaries between the North and South of China divided by the Qinling-Huaihe Line. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/Fig1.JPG"},{"id":1731623,"identity":"af9feb6c-5a04-4b49-a9ce-10533f0b3376","added_by":"auto","created_at":"2020-07-30 15:04:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44948,"visible":true,"origin":"","legend":"The prevalence of being overweight including obesity, in 11 Chinese provinces. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/Fig2.JPG"},{"id":1731624,"identity":"46efb8ad-3fb6-4c98-9fac-3d90905f5cfa","added_by":"auto","created_at":"2020-07-30 15:04:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48356,"visible":true,"origin":"","legend":"Food intake and dietary scores in the North and South of China","description":"","filename":"Fig3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/Fig3.JPG"},{"id":13560131,"identity":"1de6ac66-c368-4638-8c3c-b08056fa09d0","added_by":"auto","created_at":"2021-09-17 03:03:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":522390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/0e845492-5be3-40bd-af2e-2b2b35b3cf1a.pdf"},{"id":1731626,"identity":"ca3b45f8-ec2c-44e6-98d2-baf30982693e","added_by":"auto","created_at":"2020-07-30 15:04:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20146,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/TableS1.docx"},{"id":1731619,"identity":"06203979-da48-4718-8299-e8213b220cfc","added_by":"auto","created_at":"2020-07-30 15:04:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20146,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/TableS1.docx"},{"id":1731614,"identity":"7bddead9-558f-4276-b54f-5848e0035100","added_by":"auto","created_at":"2020-07-30 15:04:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20146,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-46188/v1/TableS1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe Difference of Being Overweight or Obese between the North and South of China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eBeing overweight or obese is an increasingly serious public health problem affecting every country. This problem is not only associated with developed countries, but has also begun to emerge in the developing world[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], especially in densely populated China. From 1991 to 2011, the percentage of overweight adults rose from 20.5\u0026ndash;42.3%, and obesity increased from 3.75\u0026ndash;11.3%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is essential to tackle this issue, as being overweight or obese has long-term influences on the development of diseases, including diabetes and hypertensive disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, it is not only the obesity phenomenon that is a growing problem; there is also the issue that Northerners have a higher prevalence of being overweight or obese than Southerners[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is generally believed that the causes of obesity are the socio-cultural environment and lifestyle[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, no data are available regarding a reduction in the prevalence of the problem from the perspective of dietary culture and regional differences, which is required information for drawing up purpose-designed policies for public health intervention.\u003c/p\u003e \u003cp\u003eIn china, the Qinling\u0026ndash;Huaihe Line is widely recognized to divide China geographically into two regions (the geographical boundaries between the North and the South of China are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the edges of certain parts of the Chinese territory not fully shown on the map) in which there are major differences in terms of the geography and environment[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Because of the geographical environment, residents\u0026rsquo; have fostered a diverse and rich dietary culture throughout their long historical activity, and developed some differences of culture and diet between the North and South of China[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Dietary patterns represent a broader prospective on the interactions between nutrients and food intake, indicating that certain patterns are high predictors of risks[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have demonstrated the relationship between dietary patterns and obesity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, to our knowledge, very few data are available concerning geographical environment and dietary culture in terms to dietary patterns and nutrient intake research in epidemic. In addition, no research has explored the impact of geographical differences and dietary culture on the associations between dietary patterns and being overweight or obese in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetermining the rates of being overweight or obese within regions and dietary patterns is very useful for tracking the progression of the epidemic and establishing the most effective places to target intervention strategies. The goal of the present study is to study the differences in rates of being overweight or obese based on the geographically influenced dietary cultures in North and South China. It is further intended to explore the causes of these differences, identifying the factors associated with geography and dietary patterns. Finally, we hypothesize that geography and dietary pattern differences are the main reasons for being overweight or obese, and we hope to identify more appropriate dietary patterns and lifestyle options to address the growing prevalence of this problem.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThe data used in this study were taken from the Chinese Health and Nutrition Survey (CHNS) for 2011. The CHNS is an ongoing, open-cohort, international, collaborative project between the University of North Carolina and the Chinese Centre for Disease Control and Prevention, and is also the only longitudinal, household-health-based survey in China[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The purpose of the CHNS is to investigate the impact of the Chinese government\u0026rsquo;s policies on family planning, health, nutrition, and implemented programs. It also provides information on health and nutritional status during the great Chinese economic transformation. These data mainly measure the impact of health behaviors, nutritional status, and outcomes, as well as changes in demographic, family, individual, economic, and social factors.\u003c/p\u003e \u003cp\u003eThe present study used a multistage random cluster to sample 10,863 residents (North\u0026thinsp;=\u0026thinsp;4,411; South\u0026thinsp;=\u0026thinsp;6,452) aged 18 to 65\u0026nbsp;years old when other samples with incomplete information were excluded. The regional variables included eleven provinces and were selected for this paper (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): in the North, this included Heilongjiang, Liaoning, Beijing, Shandong, and Henan; in the South, this included Jiangsu, Shanghai, Hubei, Chongqing, Hunan, Guizhou, and Guangxi. A small section of Henan lies south of the Qinling\u0026ndash;Huaihe Line, whereas Jiangsu and Hubei have minor regions located north of the Qinling\u0026ndash;Huaihe Line; however, we still consider Henan a Northern province and Jiangsu and Hubei Southern provinces because the Qinling\u0026ndash;Huaihe Line is not divided by the provincial boundaries, but by rivers and mountains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variables\u003c/h2\u003e \u003cp\u003eMeasurements were taken of height (m), weight (kg), and BMI (weight/height\u003csup\u003e2\u003c/sup\u003e) by trained health workers following standardized protocols from the WHO. The clinical classification criteria indicated that being overweight is defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;24\u0026nbsp;kg/m\u0026sup2; and obesity is defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u0026nbsp;kg/m\u0026sup2;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDietary patterns ware presented by dietary score. Individual food consumption was recorded through a 24-hour recall method for three consecutive days for each participant. The eating times and weight of each type of food the participants consumed over the course of three days were recorded to determine the total average consumption of each category of food per day. The dietary scores ranking the reasonable intake levels of food patterns were obtained through the Dietary Balance Index (DBI)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which is a comprehensive statistical technique for assessing under- and over-nutrition in Chinese residents, thus providing a useful tool to monitor dietary healthiness[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The DBI method is similar to the Healthy Eating Index[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and the Diet Quality Index[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The closer the dietary score is to zero, the more reasonable the dietary intake is. In contrast, the higher the dietary score is, the more likely the dietary intake equates to over-nutrition. Scoring details of the DBI method can be found in Table S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe independent variables in this study is dietary quality score and regional variables, dietary score is not only the dependent variable for former stage but also the independent variable for latter stage. The control variables were household income, education level, age, sedentary time, and some health-related information about parents collected via in-home interviews and general information questionnaires. The sample\u0026rsquo;s education level was divided into five levels (none, primary school, lower middle school, upper middle school, and university). Sedentary time includes all the sedentary activities taking place at home, such as watching TV, playing games, surfing the Internet, and reading. This study did not directly consider the variable of \u0026ldquo;average number of times per week of engaging in physical or outdoor activity\u0026rdquo; because individual exercise patterns were inconsistent. As a complement, the sedentary time variable (minutes per week) was used to control for the effect of physical activity on obesity analysis. In addition, individual registration, smoking and alcohol drinking are also considered in our present study. Participants were selected in the 18-to-65 age range to eliminate the effects of the changing dietary patterns in children or the elderly. In addition, we also considered how the family environment factors into children\u0026rsquo;s obesity and nutritional balance, such as parental dietary patterns, obesity, hypertension, and diabetes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, regional variables were defined in three different forms by the span of geographic space: regional type I, regional type II, and regional type III. Regional type I is divided into 11 areas according to the division of administrative provinces in China (the region sequentially moves from the southernmost Guangxi province to the northernmost Heilongjiang province). Regional type II is divided into three areas: North (Heilongjiang, Liaoning, Beijing, and Shandong), Central (Henan, Jiangsu, and Hubei) and South (Shanghai, Chongqing, Hunan, Guizhou, and Guangxi). Regional type III is divided into two areas: The South and the North. From regional type I to regional type III, the span of geographical space gradually increases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExtraction of dietary patterns\u003c/h2\u003e \u003cp\u003eDietary patterns were extracted using principal component factor analysis based on 12 main categories of food[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Two dietary patterns were obtained from participants\u0026rsquo; food consumption records with eigenvalues (\u0026gt;\u0026thinsp;1), and the variance explained by each factor. The sign defining these linear combinations, called factor loadings, determined the correlations of each food category with the factor. Food categories with loadings on a factor were used to describe the characteristics of dietary patterns; higher loadings mean the categories share more variance with the factor. Labelling of the factors is mainly descriptive and based on our explanation of dietary pattern structures, and participants were labelled according to dietary-pattern-specific factor scores. Scores for each dietary pattern were counted as the sum of the factor loading coefficients and standardized daily consumption of each food category associated with the corresponding pattern.\u003c/p\u003e \u003cp\u003eIn this study, we used two kinds of scores for diet: one was calculated via the DBI, and the other was obtained using factor analysis. The DBI method can comprehensively show the quality of food intake, which mainly scores the level of food intake according to the level of each food (found in Table S1), and finally obtain the dietary score through a summation. In contrast, the factor analysis method mainly indicates the characteristics of specific dietary patterns, scoring points by the weighted sum of the combination coefficients of a certain pattern. Therefore, we have mainly employed dietary score via the DBI method to compare and study the food intake of residents between the North and South, but using the score calculated via factor analysis to present the characteristics of the two newly extracted Southern and Northern dietary patterns, in the final regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical study\u003c/h2\u003e \u003cp\u003eRelated data were analyzed via Stata version 14 (Stata Corp, College Station, TX, USA) to determine the causes of the different rates of being overweight or obese between China\u0026rsquo;s North and South. First, a series of descriptive analyses were conducted regarding the participants\u0026rsquo; BMI and demographic characteristics (e.g., education level, household income, age), rates of being overweight or obese, the degree of different categories of food intake, and parental health-related conditions (e.g., BMI, hypertension, diabetes) and food intake. The DBI method was also applied as a dietary score measure to assess the quality of dietary intake, in which a high score means more food intake and over-nutrition being more likely. Comparisons of the changes in prevalence across regional type III were made using t-tests for continuous variables and χ\u003csup\u003e2\u003c/sup\u003e tests for binary variables (allowing for unequal variance between two groups). Second, we assessed correlations between various categories of food intake and regional type III, and hoped to extract two kinds of dietary patterns using factor analysis. Furthermore, we carried out a series of regression analyses to assess how regions have influenced dietary scores and being overweight or obese, adding certain control variables related to parental obesity-related information. Finally, on the basis of the existing relationships between regional variables and dietary scores, we further took being overweight or obese as a dependent variable to conduct multivariate logistic regression with the two dietary patterns extracted using factor analysis and the South\u0026ndash;North regional dummy to verify that these relationships with different dietary patterns and geographical variations have a significant impact on rates of being overweight or obese.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eSignificant increases in mean BMI were observed when moving from the more southern to the more northern areas (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The BMI in the North was 23.732%, whereas in the South, it was 22.694% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Household income, education level, and sedentary time also exhibited statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which was lower than the significance level for BMI. In addition, both Southern parents\u0026rsquo; BMI levels were higher than those of parents living in the North (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; all p\u0026thinsp;\u0026le;\u0026thinsp;0.001 for BMI), and a portion of the parents\u0026rsquo; obesity-related diseases also showed significance between the two regions. These differences in BMI for both participants and their parents demonstrated that there was a highly significant difference in rates of being overweight or obese between the South and North of China.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSamples\u0026rsquo; BMI and demographic characteristics along with their parents\u0026rsquo; health-related information\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNumber\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNorth\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSouth\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome (yuan)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducated level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSedentary time (minutes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e403.923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e453.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePercentage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of mother\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.154\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of mother\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of father\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of father\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we calculated the rates of being overweight or obese in 11 provinces. From the Northern areas to the Southern areas, the prevalence shows a roughly decreasing trend (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, the average rate of being overweight including obese in the North was higher than that in the South by 10.51%. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e portrays how there are significant differences in the rates of being overweight including obese between the North and South of China.\u003c/p\u003e\n\u003cp\u003eAs there are significant differences in the rates of being overweight including obese between the North and South, we further verified whether there is a difference in dietary patterns. Analyzing the statistical description of food intake and dietary scores, we observed that most categories of food feature significant differences between the two areas (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Intake of rice, vegetables, poultry, meat, fish, and alcohol in the South was greater than that in the North, and statistical significance was reached for all categories except poultry and alcohol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). In contrast, intake of wheat, soybeans, fruits, tubers, eggs, and dairy in the North was higher than that in the South, and statistical significance was reached except for soybeans (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). In addition, rice and wheat were the largest difference in food categories between the North and South; the average consumption of rice in the South was 165.9\u0026nbsp;g higher than that in the North, whereas the average consumption of wheat in the North was 158.5\u0026nbsp;g higher than that in the South. Furthermore, there were also significant differences in dietary scores between the North and South. The dietary scores of Northerners were higher by 1.1 points than those of Southerners on average, which means that the total quality of food intake in the North is more likely to reflect over-nutrition than in the South. In conclusion, there are significant differences in dietary patterns among residents between the North and South.\u003c/p\u003e\n\u003cp\u003eIn addition, we found that most categories of food were highly correlated with regional variables from the South to the North (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Wheat, soybeans, fruits, tubers, eggs, and dairy had significantly positive correlations with regional variables, whereas rice, vegetables, poultry, meat, and fish had significantly negative correlations with regions, demonstrating that there were significant differences with respect to geographical variation in the dietary patterns of Chinese residents. In addition, the estimated coefficient of wheat and rice was the largest but opposite, which reveals that the difference between wheat and rice was the most significant between the two areas, and such results were consistent with Song\u0026rsquo;s findings[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation analysis between different categories of food with regions\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWheat\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSoybean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFruits\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTuber\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEgg\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDairy\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type\u0026nbsp;III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.451***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.229***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoultry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.364***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.210***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.119***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.292***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.115***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we extracted two types of dietary patterns through factors analysis, and they were defined as Southern dietary patterns and Northern dietary patterns respectively based on regional characteristics of food consumption. Southern dietary patterns were rich in rice, vegetables, meat, poultry, etc.; Northern dietary patterns were abundant in wheat, soybeans, etc. The attribution of each category of food was based on the rotation component matrix from the factor analysis (Table\u0026nbsp;3). Finally, we obtained factor scores of the two types of dietary patterns using the sum of the factor loading coefficients and standardized daily consumption of each food category, and we defined them as new variables for Southern dietary patterns and Northern dietary patterns, which is generally consistent with the actual information surrounding the consumption of various types of food in the two regions[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 3. \u003cbr /\u003eWeighting combinations of the two dietary patterns extracted from factor analysis\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSouthern dietary patterns\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNorthern dietary patterns\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRice\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.175\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.421\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.337\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoultry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.348\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWheat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.558\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSoybean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.507\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFruits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTuber\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDairy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.090\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEgg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSnack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to statistics, there are indeed significant differences between the North and South of China with respect to dietary patterns and rates of being overweight or obese. Therefore, we further assessed the causes of these differences through empirical research.\u003c/p\u003e\n\u003cp\u003eAll of these estimated coefficients for regional variables had a significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with dietary score (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and this gradually increased from 0.287 to 1.494 with a rise in geographic span (from model 1 to model 3). In model 1, the geographic span gradually changed from South to North according to adjacent provinces for all 11 provinces. Furthermore, in model 2, the geographic span was first changing from the Southern to Central areas, and then to the Northern areas, and the size of the geographic span was larger than regional type I. Finally, in model 3, the geographic span changed directly from South to North, adjusted for the three Central provinces (Henan, Jiangsu, and Hubei) into Northern and Southern areas mainly according to their geographical locations, and the geographic span of regional types III were the greatest than that of regional types I and II. In conclusion, the larger the geographic span, the greater the difference in dietary scores. Furthermore, the dietary score rose from the South to the North, indicating that Southerners\u0026rsquo; dietary patterns were less prone to over-nutrition than those of Northerners. After adjusting for income, age, education level, sedentary time, registration, smoking, drinking and parents\u0026rsquo; dietary scores, the estimated coefficient of the regions was still significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Moreover, education level and sedentary time were significantly negatively correlated with dietary scores, which was consistent with Woo\u0026rsquo;s results[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The samples\u0026rsquo; dietary scores were also significantly correlated with those of their parents, which may be because there is a similar dietary difference in generations between the North and South[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLinear regression analysis for regions with respect to dietary scores\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 4\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type I\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.287***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.076***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.494***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.384**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.215**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducated level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.169**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSedentary time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.0003*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s dietary scores\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.633***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s dietary scores\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.207***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes or no\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.264\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.201***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.277***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.693***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.341*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Model 1, model 2 and model 3 only considered the different sizes of geographic span to conduct regression analysis of diet scores. Model 4 included all of model 3 and was further adjusted for household income, age, education level, sedentary time and parents\u0026rsquo; dietary scores. It should be noted that a large number of samples are lost in the regression model of 4 (n\u0026thinsp;=\u0026thinsp;1,515 residents (North\u0026thinsp;=\u0026thinsp;565; South\u0026thinsp;=\u0026thinsp;986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we evaluated the causes of being overweight or obese regarding rate differences through regional factors. All of the odds ratios (OR) for regional variables exhibited a significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with being overweight or obese (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e); the OR for being overweight gradually increased from 0.923 to 1.681, and the OR for obesity gradually rose from 0.902 to 2.035 with the increase in geographic span (from model 5 to model 7 and from model 9 to model 11). The variation trend of the OR coefficient was consistent, as per Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. After adjusting for income, age, education level, sedentary time, and parents\u0026rsquo; obesity-related information, the regional OR coefficients were still of significance. In conclusion, the possibility of becoming overweight or obese increases gradually from the Southern areas to the Northern areas, and the greater the geographic span, the faster these rates increase. Furthermore, the samples\u0026rsquo; rates of being overweight or obese were also significantly correlated with those of their parents\u0026rsquo; obesity-related history.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate adjusted odds ratios for regions with respect to being overweight or obesity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eOverweight\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eObesity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 7\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 8\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 9\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 10\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 11\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 12\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type I\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.923***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.902***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.345***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.482***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.681***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.319*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.035***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.541*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.341*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.038***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.038**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducated level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.976\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSedentary time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes or no\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.360\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.359*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.498\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.497*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.048**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.778***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.735*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.150***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.007***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.164\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.403***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.473***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.670***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.030***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.263***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.064***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.100***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Models 5 to 7 and models 9 to 11 only considered the different sizes of the geographic span for being overweight and obesity, respectively. Model 8 and model 12 includes all of model 7 and model 11 respectively, and further adjusts for control variables household income, age, education level, sedentary time and parents\u0026rsquo; histories of obesity and chronic health-related information. It should be noted that a large number of samples are lost in the regression model of 8 and 12 (n\u0026thinsp;=\u0026thinsp;1,515 residents (North\u0026thinsp;=\u0026thinsp;565; South\u0026thinsp;=\u0026thinsp;986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the aforementioned studies, there are significant differences in dietary patterns and rates of being overweight or obese for residents of the two regions. Southern rates of being overweight or obese and dietary scores were lower than those of the North. Therefore, we further verified the influence of both dietary differences and geographical differences in terms of the problem of residents being overweight or suffering from obesity. In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, we show the two new dietary patterns extracted using factor analysis, which can approximately represent the characteristics of Northern and Southern dietary patterns rather than artificially dividing two local dietary patterns by geographical boundaries.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate adjusted odds ratios for dietary patterns and regions with respect to being underweight or suffering from obesity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eOverweight\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eObesity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 13\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 14\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 15\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 16\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 17\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 18\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 19\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 20\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional type III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.464***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.697**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorthern dietary patterns\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.226***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.331***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.223***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.258***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.333***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.199*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouthern dietary patterns\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.167**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.242***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.875***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.083\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.095\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.038***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.039***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducated level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSedentary time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes or no\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.272*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.282**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.263\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.820***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.758***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.218***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.116***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.374**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.364**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.207\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMother\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.888\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.241***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.099***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.987***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.746***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.080\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFather\u0026rsquo;s diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.872\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.780***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.781***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.096***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.088***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.132***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.134***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Models 13 to 14 and models 17 to 18 only considered the different dietary patterns for being overweight and obesity, respectively. Models 15 and 19 feature logistic regression analysis with both new two dietary patterns. Model 16 and 20 includes all of model 15 and 19 respectively, and further adjusts for household income, age, education level, sedentary time, parents\u0026rsquo; history of obesity and chronic health-related information for regional type III. It should be noted that a large number of samples are lost in the regression model of 15, 16 ,19 and 20 (n\u0026thinsp;=\u0026thinsp;1,515 residents (North\u0026thinsp;=\u0026thinsp;565; South\u0026thinsp;=\u0026thinsp;986)), because these models include parental factors, and the matching steps between the samples and their elders are difficult.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNorthern dietary patterns exhibited a significant OR for being overweight of 1.226 and for being obese of 1.258. Southern dietary patterns only showed a significant effect for being obese of 0.875. Moreover, after adjusting for income, age, education level, sedentary time, and parents\u0026rsquo; history of obesity and chronic health-related information, the OR coefficient for Northern dietary patterns was still significant for being overweight or obese. Finally, we allotted the new two dietary patterns and regional type III to models 16 and 20, with the results showing that the OR coefficient of both regions and Northern dietary patterns exhibited a significant correlation with being overweight or obese, but Southern dietary patterns only had a significant coefficient for being overweight, demonstrating that Northern dietary patterns is more likely to cause obesity. The estimated coefficient of Northern dietary patterns was larger than that of Southern dietary patterns for being overweight, and Southern dietary patterns only had a significant effect on being overweight. All of these phenomena and estimated coefficients indicate that Northern dietary patterns have a greater and stable influence on being overweight or suffering from obesity. These phenomena also suggest that geographical differences not only directly affect the prevalence of being overweight or obese, but also indirectly impact the prevalence of being overweight or obese through influencing dietary patterns between the two areas.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eBeing overweight or obese is one of the most important social and health problems, and its prevalence is increasing continuously worldwide, including in China[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Currently, it is generally believed that lifestyle and diet are the primary causes of being overweight or suffering from obesity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and that the future solution for such epidemiological issues should come from political policies based on prevention of those aspects related to health and social customs surrounding being overweight or obese. However, not enough is yet known about these factors, which furthermore may vary from place to place[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, we systematically analyzed the differences in dietary patterns and the prevalence of being overweight or obese between the South and North of China. The prevalence of being overweight or obese in the South was 10.51% less than that of the North, with this prevalence falling when moving through regions from Northern areas to Southern areas. Moreover, the Southerners\u0026rsquo; dietary score regarding the quality of food consumed was also lower by 1.1% than that of Northerners. People in the North have a high intake of wheat, tubers, fruits, etc., whereas individuals in the South eat more rice, vegetables, meat, poultry, etc. From the South to the North, all of these estimated coefficients regarding dietary patterns and being overweight or obese were positively and significantly correlated with the geographical variable. In addition, with the spatial span increase defined by three regional types from the South to the North (neighboring provinces, South\u0026ndash;Central\u0026ndash;North areas, and South\u0026ndash;North areas), the estimated coefficients for dietary score increased from 0.287 to 1.494, the OR for being overweight rose from 0.923 to 1.681, and the OR for obesity increased from 0.902 to 2.035. Finally, after placing regional variables and the two dietary patterns extracted using factor analysis into the same model, the OR of the three variables for being overweight were statistically significant at 1.464, 1.223 and 1.242, respectively, but only the OR of Northern dietary patterns and regional type III for obesity were statistically significant at 1.697 and 1.199, respectively. In short, residents from the South were also less likely to be overweight or obese than those from the North before and after adjustment for individual socio-demographic characteristics and household factors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirstly, there are significant differences in dietary patterns between the North and South of China. One plausible contributor is regional differences in the geographical environment. China has a natural, geographical dividing line\u0026mdash;the Qinling\u0026ndash;Huaihe Line, which consists of the Qinling Mountains and Huaihe River\u0026mdash;which leads to differences between the North and South in many respects[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This natural dividing line is also a dividing line in terms of climate. In the North, there is a temperate monsoon climate with little rainfall. In contrast, in the South, there is subtropical monsoon climate with plenty of rain[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Owing to the difference in climate between the North and South, the residents\u0026rsquo; farming methods are also different. Residents in the North mainly cultivate drought-tolerant crops, such as wheat, whereas residents in the South primarily grow subtropical cash crops, including rice and tea[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As a result, over the course of history, residents have established a variety of dietary cultures with local geographic characteristics, and the types of local agricultural products available may be responsible for some of the differences in dietary patterns[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For example, wheat became a main staple food in the North, and rice a major staple in the South. The present study\u0026rsquo;s findings are consistent with the actual geographical and cultural characteristics of China as well as findings of previous works[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, we applied factor analysis to extract two dietary patterns that roughly represent the difference in dietary characteristics between the North and South; these extracted dietary patterns also show that Southern dietary patterns are rich in rice, vegetables, meat, and poultry, whereas Northern dietary patterns are rich in wheat, soybeans, etc. A number of studies have established that the \u0026ldquo;modern high-wheat\u0026rdquo; pattern is rich in fat and induces a high risk of obesity and metabolic syndromes, whereas \u0026ldquo;vegetable-rich\u0026rdquo; and \u0026ldquo;healthy patterns\u0026rdquo; have had no associations with metabolic syndromes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Zhang\u0026rsquo;s work showed that traditional Southern dietary patterns feature a high intake of vegetables, fish, and meat, or what constitutes a balanced dietary pattern that may help to reduce the risk of chronic diseases, such as abdominal obesity and hypertension[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, climate conditions may contribute to the differences in nutritional intake. The North is colder than the South, and as such, Northerners need to take in more energy to resist the cold, so their total food consumption is more than that in the South[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Meanwhile, Southern residents usually eat coarse fiber foods, such as corn and buckwheat, which can easily enhance a sense of satiety and control the amount of energy consumed[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As a result, the differences in dietary patterns may be contributed to the geographical differences in climate and culture.\u003c/p\u003e \u003cp\u003eSecondly, the odds of being overweight or obese change rapidly and with positive significance as the geographic variables moving from South to North. When the region variable sequentially moves from one province to another across all 11 provinces from the Southern areas to the Northern areas, the prevalence of being overweight or obese slowly rises, but it increases quite quickly when the region variable directly moves from the Southern to the Northern areas. These phenomena and positive estimated coefficients indicated that the prevalence of being overweight or obese is significantly correlation with regional variables. This may also be explained by differences in physical activity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The difference between energy intake and output partial contributed by physical activity means that rates of being overweight or obese are higher in the North than in the South[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another reasonable contributing factor may be dietary patterns. The present findings indicate that Chinese residents established local characteristics of lifestyle and diet cultures throughout long-term historical activities because of the existence of a boundary line between the North and South, which further affects residents\u0026rsquo; dietary patterns. Overall, differences in residents\u0026rsquo; rates of being overweight or obese may derive from characteristics of the long-established culture or lifestyle as impacted by an individual\u0026rsquo;s residence, features of the dietary culture, and the regions themselves; persons in the North have greater eating scores and are more overweight or obese than those in the South. Swinburn and Egger have outlined an ecological framework for understanding rates of being overweight or obese, and they distinguished specific types of environments, including physical, economic, and socio-cultural environments[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Hawkins and Griffiths found that the United States\u0026rsquo;s geography affects early childhood rates of being overweight through dependence after adjustment for individual socio-demographic characteristics[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Angela also demonstrated that pre-adolescent children in Germany are considered overweight with marked geographical differences in prevalence[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Regions with unique environmental, cultural, or policy-related (e.g., infrastructure) traits may provide or support opportunities for physical activity and/or access to a healthy diet, which have different influences on the prevalence of being overweight or obese.\u003c/p\u003e \u003cp\u003eBased on the geographical differences and dietary habits formed throughout its long history, there are certain dietary and cultural differences between Northern and Southern China. However, the differences may not only be seen in contemporary residents, but also their elders, and the present study determined that Northern elderly residents have higher rates of being overweight or obese and dietary scores than those of Southern elders. The rates of fathers and mothers being overweight or obese in the North are 19.66% and 18.81% higher than those in the South, respectively, and the dietary scores of those in the North are 1.72 and 0.88 points higher than those in the South, respectively. Furthermore, parents\u0026rsquo; rates of obesity had a positive and significant correlation with participants\u0026rsquo; rates of being overweight or obese, and parents\u0026rsquo; dietary scores were also significantly correlated with participants\u0026rsquo; dietary scores. This difference and this correlation indicate that geographical differences may have a significant impact on dietary patterns for each generation, and local dietary patterns and geographical differences further affect residents\u0026rsquo; nutritional balance[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, residents\u0026rsquo; household income, age, education level, and sedentary time also exerted some influence on the prevalence of being overweight or obese. Persons with higher household income, age, and sedentary time were more likely to be obese, but those with higher education levels were less prone to obesity[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Currently, with the rapid increasingly economy and concomitant changes in lifestyle, there has been a markedly high intake of fast foods in China[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, because most fast foods have high fat and energy levels, thus leading a greater risk of hypertension, glucose, and being overweight or obese[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It is necessary to be aware of the problems of rapidly changing dietary patterns and obesity-related diseases.\u003c/p\u003e \u003cp\u003eThe present research has several strengths compared to other studies. We selected 11 representative provinces to demarcate the differences in dietary patterns and rates of being overweight or obese between the North and South of China, and for the first time, we studied the causes of these differences in an empirical manner considering geographical differences and dietary patterns, specifically taking into account traditional Chinese culture[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, we comprehensively described specific dietary patterns of residents from the North and South while comparing the differences in the dietary structure of residents by the method of DBI, which could serve as a robust predictor of being overweight or obese and lead to effective policy interventions. Furthermore, the study provides information regarding the relationships among the South\u0026ndash;North geographical difference, dietary patterns, and the prevalence of being overweight or obese. Northerners in the study ate differently from Southerners, at least from a qualitative perspective, probably consuming more food and leading to more opportunities for obesity. Finally, based on the natural geographical dividing lines in China, we discussed the historical origin of the differences in residents\u0026rsquo; dietary patterns and the causes of the differences in rates of being overweight or obese, combining the characteristics of an individual\u0026rsquo;s living environment and traditional culture. Our results also suggest that a series of relevant policies should be put in place to support the improvement of dietary patterns and living habits, thereby removing barriers to healthy eating and nutritional balance.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study did exist. We did not specifically evaluate the factors of economic status, family environment, physical activity, education level, registration, smoking, drinking, or genetic factors, and simply accounted for them as control variables in this paper. Further work is necessary to consider differences in the surrounding environment between the North and South to determine how these differences may promote a healthier nutritional balance. In addition, because of the limited information on nutrition, we did not assess the differences in nutritional composition between Northerners and Southerners. Certainly, there are other factors that may cause differences in the problems of being overweight or obese.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThere are indeed significant differences in dietary patterns and obesity issues between the North and South of China. Northern dietary scores and the rates of being overweight or obese were higher than those of Southern China. Our findings suggest that China\u0026rsquo;s natural geographical difference between the North and South may lead to different dietary patterns starting with local characteristics established throughout a long-term history, then geographical differences and formed local dietary patterns eventually resulting in different rates of being overweight or obese between the two regions. This provides empirical evidence for policies that target the \u0026lsquo;\u0026lsquo;obesogenic\u0026rsquo;\u0026rsquo; environment and promote opportunities for persons to access healthy dietary patterns and nutritional balance.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eBMI = body mass index; OR = odds ratio; DBI = dietary balance index; Chinese Health and Nutrition Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u0026nbsp;Additionally, this article also obtained the ethics approval by the ethical review board of Beijing Jiaotong University. All authors agreed to participate and publicise related study in the paper, and informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot required\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available from the China Health and Nutrition Survey (CHNS), and the datasets used and/or analyzed during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there is no potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by a grant-in-aid for Chinese National Funding of Social Sciences (Grant 18ZDA086) and a grant-in-aid for Key Program of Beijing Social Science Foundation (Grant 17GLA006). This funding body did not play any role in the design of the study and in the writing the manuscript but in the collection of data and charges providing.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to this work. Daisheng Tang and Xuefan Dong worked on the study design and method. Tao Bu was respondsible for manuscript design, data analysis and paper writing while Daisheng Tang and Xuefan Dong supervised analysis again. Yahong Liu provide interpretive input and words correcting. All authors critically reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThank for Chinese Center for Disease Control and Prevention to provide China Health and Nutrition Survey (CHNS) data supporting. Thanks to all the subjects involved in the experiment, which agree to free use the data free and to publish relevant data or reports.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePoobalan, A. and L. Aucott, Obesity Among Young Adults in Developing Countries: A Systematic Overview. Curr Obes Rep, 2016. 5(1): p. 2-13.\u003c/li\u003e\n\u003cli\u003eMi, Y. J., Zhang, B., Wang, H. J., Yan, J., Han, W., Zhao, J., Tian, Q. B. (2015). Prevalence and Secular Trends in Obesity Among Chinese Adults, 1991-2011. American Journal of Preventive Medicine, 49(5), 661-669\u003c/li\u003e\n\u003cli\u003eOutbreaks, D. and O. Complications, Obesity epidemic puts millions at risk from related diseases. Israel Journal of Medical Sciences, 1997. 33(10).\u003c/li\u003e\n\u003cli\u003eWildman, R.P., et al., Trends in overweight and obesity in Chinese adults: between 1991 and 1999-2000. Obesity, 2012. 16(6): p. 1448-1453.\u003c/li\u003e\n\u003cli\u003eYu, Z., et al., Obesity related metabolic abnormalities: Distribution and geographic differences among middle-aged and older Chinese populations. Preventive Medicine, 2009. 48(3): p. 272-278.\u003c/li\u003e\n\u003cli\u003eGu, D., et al., Prevalence of the metabolic syndrome and overweight among adults in China. Lancet, 2005. 365(9468): p. 1398-1405.\u003c/li\u003e\n\u003cli\u003eHill, J.O. and J.C. Peters, Environmental contributions to the obesity epidemic. Science, 1998. 280(5368): p. 1371-1374.\u003c/li\u003e\n\u003cli\u003eHe, K., et al., Overview of karst geo-environments and karst water resources in north and south China. Environmental Earth Sciences, 2011. 64(7): p. 1865-1873.\u003c/li\u003e\n\u003cli\u003eSong, F. and M.S. Cho, Geography of Food Consumption Patterns between South and North China. Foods, 2017. 6(5).\u003c/li\u003e\n\u003cli\u003eXianwen, S., et al., Dietary pattern and its association with the prevalence of obesity and related cardiometabolic risk factors among Chinese children. Plos One, 2012. 7(8): p. e43183-e43183.\u003c/li\u003e\n\u003cli\u003eJing, S., B. Nicholas and H. Andrew, Dietary Pattern and Its Association with the Prevalence of Obesity, Hypertension and Other Cardiovascular Risk Factors among Chinese Older Adults. International Journal of Environmental Research \u0026amp; Public Health, 2014. 11(4): p. 3956-3971.\u003c/li\u003e\n\u003cli\u003eKuhnlein, H.V., et al., Arctic Indigenous Peoples Experience the Nutrition Transition with Changing Dietary Patterns and Obesity. Journal of Nutrition, 2004. 134(6): p. 1447-1453.\u003c/li\u003e\n\u003cli\u003eZhang, B., et al., The China Health and Nutrition Survey, 1989-2011. Obesity Reviews, 2013. 15(S1): p. 2-7.\u003c/li\u003e\n\u003cli\u003eEveleth and B. Phyllis, Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. Geneva Switzerland Who, 1995. 854(6): p. 1-452.\u003c/li\u003e\n\u003cli\u003eXiaoyue, X., et al., Assessing dietary quality of older Chinese people using the Chinese Diet Balance Index (DBI). Plos One, 2015. 10(3): p. e0121618.\u003c/li\u003e\n\u003cli\u003eZhang, Q., et al., Dietary Balance Index-07 and the Risk of Anemia in Middle Aged and Elderly People in Southwest China: A Cross Sectional Study. Nutrients, 2018. 10(2): p. 162.\u003c/li\u003e\n\u003cli\u003eAdam, D. and C.D. Rehm, Consumption of low-calorie sweeteners among U.S. adults is associated with higher Healthy Eating Index (HEI 2005) scores and more physical activity. Nutrients, 2014. 6(10): p. 4389-4403.\u003c/li\u003e\n\u003cli\u003eHaines, P.S., A.M. Siegariz and B.M. Popkin, The Diet Quality Index revised: a measurement instrument for populations. Journal of the American Dietetic Association, 1999. 99(6): p. 697-704.\u003c/li\u003e\n\u003cli\u003eNewby, P.K., Are dietary intakes and eating behaviors related to childhood obesity? A comprehensive review of the evidence. Journal of Law Medicine \u0026amp; Ethics, 2010. 35(1): p. 35-60.\u003c/li\u003e\n\u003cli\u003eBatis, C., et al., Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults. Public Health Nutrition, 2016. 19(2): p. 195-203.\u003c/li\u003e\n\u003cli\u003eWoo, J., et al., Influence of educational level and marital status on dietary intake, obesity and other cardiovascular risk factors in a Hong Kong Chinese population. European Journal of Clinical Nutrition, 1999. 53(6): p. 461-467.\u003c/li\u003e\n\u003cli\u003eStafleu, A., et al., Family resemblance in energy, fat, and cholesterol intake: a study among three generations of women. Preventive Medicine, 1994. 23(4): p. 474.\u003c/li\u003e\n\u003cli\u003eXu, X., et al., Dietary Pattern Is Associated with Obesity in Older People in China: Data from China Health and Nutrition Survey (CHNS). Nutrients, 2015. 7(9): p. 8170-8188.\u003c/li\u003e\n\u003cli\u003eJia, P., et al., Time Trend and Demographic and Geographic Disparities in Childhood Obesity Prevalence in China-Evidence from Twenty Years of Longitudinal Data. International Journal of Environmental Research \u0026amp; Public Health, 2017. 14(4): p. 369-.\u003c/li\u003e\n\u003cli\u003eReynolds, K., et al., Prevalence and Risk Factors of Overweight and Obesity in China*. Obesity, 2012. 15(1): p. 10-18.\u003c/li\u003e\n\u003cli\u003eSherpa, L. Y., Stigum, H., Chongsuvivatwong, V., Thelle, D. S., \u0026amp; Bjertness, E. (2010). Obesity in Tibetans Aged 30? Living at Different Altitudes under the North and South Faces of Mt. Everest. International Journal of Environmental Research \u0026amp; Public Health, 7(4), 1670-1680.\u003c/li\u003e\n\u003cli\u003eWang, Z., W. Fang and Y. Liao, Assessment of physical vulnerability to agricultural drought in China. Natural Hazards, 2013. 67(2): p. 645-657.\u003c/li\u003e\n\u003cli\u003eCheng, C., J. Ren and R. Wang, Spatial-temporal distribution of agricultural eco-efficiency in China. Acta Ecologica Sinica, 2014. 34(1).\u003c/li\u003e\n\u003cli\u003eGracey, M., Historical, cultural, political, and social influences on dietary patterns and nutrition in Australian Aboriginal children. American Journal of Clinical Nutrition, 2000. 72(5 Suppl): p. 1361S-1367S.\u003c/li\u003e\n\u003cli\u003eHui, C., et al., A prospective study of dietary patterns and mortality in Chinese women. Epidemiology, 2007. 18(3): p. 393-401.\u003c/li\u003e\n\u003cli\u003eZhang, J.G., et al., Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women. European Journal of Clinical Nutrition, 2015. 69(9): p. 1009.\u003c/li\u003e\n\u003cli\u003eAya, Health differences between north and south caused by five reasons[J]. World of labor security, 2016. (13):70-70. [In Chinese].\u003c/li\u003e\n\u003cli\u003eJian, Z., C. Wang and J. Gao, Analysis on the dietary lipid pattern in different areas of China. Journal of Hygiene Research, 1999. 28(6): p. 359.\u003c/li\u003e\n\u003cli\u003eWu, X., et al., Changes in average blood pressure and incidence of high blood pressure 1983-1984 to 1987-1988 in four population cohorts in the People's Republic of China. The PRC-USA Cardiovascular and Cardiopulmonary Epidemiology Research Group. Journal of Hypertension, 2012. 14(11): p. 1267-74.\u003c/li\u003e\n\u003cli\u003eZhou, B., et al., The relationship between dietary factors and serum lipids in Chinese urban and rural populations of Beijing and Guangzhou. PRC-USA Cardiovascular and Cardiopulmonary Research Group. International Journal of Epidemiology, 1995. 24(3): p. 528.\u003c/li\u003e\n\u003cli\u003eEgger, G. and B. Swinburn, An \"ecological\" approach to the obesity pandemic. Bmj, 1997. 315(7106): p. 477-480.\u003c/li\u003e\n\u003cli\u003eSwinburn, B., G. Egger and F. Raza, Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 1999. 29(6 Pt 1): p. 563.\u003c/li\u003e\n\u003cli\u003eHawkins, S., et al., Regional differences in overweight: an effect of people or place? Archives of Disease in Childhood, 2008. 93(5): p. 407-413.\u003c/li\u003e\n\u003cli\u003eLiese, A.D., et al., Burden of overweight in Germany: prevalence differences between former East and West German children. European Journal of Public Health, 2006. 16(5): p. 526.\u003c/li\u003e\n\u003cli\u003ePineda, E. and J. Mindell, Association of the food environment with obesity: a systematic review of geographical and statistical methods. Lancet, 2016. 388: p. S89.\u003c/li\u003e\n\u003cli\u003eThornton, L.E., J.R. Pearce and A.M. Kavanagh, Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary. International Journal of Behavioral Nutrition \u0026amp; Physical Activity, 2011. 8(1): p. 71.\u003c/li\u003e\n\u003cli\u003eChang, V.W. and D.S. Lauderdale, Income disparities in body mass index and obesity in the United States, 1971-2002. Archives of Internal Medicine, 2005. 165(18): p. 2122-2128.\u003c/li\u003e\n\u003cli\u003eGouda, J. and R.K. Prusty, Overweight and obesity among women by economic stratum in urban India. Journal of Health Population \u0026amp; Nutrition, 2014. 32(1): p. 79-88.\u003c/li\u003e\n\u003cli\u003eMonteiro, C.A., W.L. Conde and B.M. Popkin, Independent effects of income and education on the risk of obesity in the Brazilian adult population. Journal of Nutrition, 2001. 131(3): p. 881S.\u003c/li\u003e\n\u003cli\u003eCheng, T.O., Effects of fast foods, rising blood pressure and increasing serum cholesterol on cardiovascular disease in china. American Journal of Cardiology, 2006. 97(11): p. 1676-1678.\u003c/li\u003e\n\u003cli\u003eMohamed, S., Functional foods against metabolic syndrome (obesity, diabetes, hypertension and dyslipidemia) and cardiovascular disease. Trends in Food Science \u0026amp; Technology, 2014. 35(2): p. 114-128.\u003c/li\u003e\n\u003cli\u003eZhang, Y. X., Wang, Z. X., Zhao, J. S., \u0026amp; Chu, Z. H. (2016). Prevalence of Overweight and Obesity among Children and Adolescents in Shandong, China: Urban and rural Disparity. Journal of Tropical Pediatrics, 62(4), 293\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Overweight, Obesity, Geographical distribution, Dietary patterns, South-North division of China","lastPublishedDoi":"10.21203/rs.3.rs-46188/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-46188/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e The geographical environment, dietary culture, food patterns, and obesity rates are substantially different between the North and South of China. Determining the geographical distribution and local dietary patterns involved in being overweight or obese is useful for designing intervention strategies. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Residents between 18 and 65 years old (n=10,863) from 11 Chinese provinces (five Northern provinces and six Southern provinces) were selected to compare dietary patterns, BMI, and health-related information from the China Health and Nutrition Survey packages in 2011. Linear and logistic regression analyses were performed to assess the strength of the association among geographic variables, the obesity problem, and dietary patterns. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The overall prevalence of being overweight or obese was 10.51% higher in the North than in the South. Northern dietary patterns feature a high intake of wheat and soybeans, whereas Southern dietary patterns feature a high intake of rice, vegetables, meat, and poultry. The estimated coefficient of regional variables surrounding dietary score is 1.494; surrounding the odds ratio for being overweight is 1.681, whereas surrounding the odds ratio for obesity is 2.035. Multivariate logistic regression including both the variable of South–North areas and Northern dietary patterns showed a significant correlation with being overweight or obese. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Northern areas and their local dietary patterns are more likely to contribute to being overweight or obese. These findings provide support for tracking the progression of obesity, epidemics, and policies that target the ‘‘obesogenic’’ environment, promoting opportunities for persons to access healthy dietary patterns and nutritional balance.\u003c/p\u003e","manuscriptTitle":"The Difference of Being Overweight or Obese between the North and South of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-07-30 15:04:48","doi":"10.21203/rs.3.rs-46188/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bada712-98a2-4f5a-865e-2b8b7cb9dd76","owner":[],"postedDate":"July 30th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":219484,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-07-30T15:04:48+00:00","versionOfRecord":[],"versionCreatedAt":"2020-07-30 15:04:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-46188","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-46188","identity":"rs-46188","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}