Using 2015 CHNS data we developed a measure that captures dietary urbanization and is positively associated with overall urbanization. Our analysis of associations between each of the individual dietary variables of interest and overall urbanization effectively informed our decisions about inclusion and exclusion of variables, and appropriate scoring of variables for generation of a set of urbanized diet indices. The set of six slightly different composite urbanized diet indices allowed for comparison of each measure’s association with overall urbanization. We determined that the urbanized diet index that was the best indicator of diet urbanization included the ownership of a refrigerator and microwave, percent of calories consumed from fruit, nuts and seeds, all snack foods, sweet snacks, eggs, dairy, fried food, away-from-home eating, high fat meat, carbohydrates, animal source foods and processed foods, as well as daily average number of snacks consumed, and daily average number of food groups consumed. The diet index excluded wine consumption. This decision was based on the little impact on the association with overall urbanization upon exclusion of the wine consumption variable, and a large reduction in percent of participants missing data. When adjusting for overall urbanization index, we saw no associations between the urbanized diet index and three CMDs - HTN, overweight and T2DM.
We saw a positive association between the urbanized diet index and overall urbanization index (R2 = 0.17 (0.01 SE)). This suggests that the urbanized diet index does not solely represent urbanization and provides distinct information, specifically about diet. We assessed the stability of the final derived urbanized diet index across sociodemographic factors, finding comparatively lower predictive accuracy by overall urbanization, age, region, energy intake, educational attainment, and income, thus suggesting some variation in ability to discriminate according to sociodemographic factors.
Some of the results of our study were unanticipated. We expected that sodium consumption would be an important factor in diet urbanization, but our findings suggest otherwise. In preliminary analysis we saw only slight changes in sodium intake with changing urbanization. This led us to fully exclude the sodium variable from the index development process. This inconsistency may be related to differences in sources of sodium in China based on level of urbanization. While salt was traditionally used as a preservative, this method is being replaced by refrigeration, which becomes more available with increasing urbanization. Yet, high sodium intake persists mainly due to salt added in cooking and increased consumption of high sodium processed foods, especially in higher urbanization areas (23)(24). In addition, reduced fiber intake is considered a factor in the nutrition transition (1). However, in preliminary analyses we saw no trend between increasing urbanization and fiber intake, thus the fiber variable was not included in the index development. This may be due to increased consumption of foods high in fiber, like fruit, to make up for reduced consumption of whole grains in high urbanization communities (25).
Additionally, we saw inconsistent associations between each of the snack-related variables (number of snacks consumed, percent of calories from all snack foods, and percent of calories from sweet snack foods) and overall urbanization. While RRRs for associations with overall urbanization steadily increased with increasing number of snacks consumed, RRRs remained stable for percent of calories consumed from all snack foods and sweet snacks, indicating that the behavior of snacking may be a greater factor in diet urbanization than the proportion of calories consumed from snacks. This could be explained by differences in what data was used to construct these dietary variables. For the three-day dietary recall, individuals were asked to classify all foods they ate by meal, specifically breakfast, lunch, dinner or snack. Number of snacks was based on the number of times an individual reported they ate a snack, or a meal outside of breakfast, lunch or dinner. This snack could be comprised of one or more types of food groups. Earlier work in this cohort has suggested, for example, that fruits and beverages are often reported as snacks (26). However, percent of calories from snacks or sweet snacks were based on food group coding. Therefore, fruits and beverages, which constitute their own food group, would not be included in this category; however, foods like chips and snack cakes are included. This suggests while these measures might be correlated, they are capturing different components of an urbanized diet and therefore could easily result in differences in the association with urbanization.
We expected that HTN, overweight, and T2DM would be associated with urbanized diet, but we saw no association in models adjusted for overall urbanization. Though we found a positive association with T2DM in the fully adjusted model with no control for overall urbanization. It is important to consider that the index we generated does not measure quality of diet, thus a more urbanized diet does not necessarily indicate a less nutritious diet, which would suggest an association with CMD. For example, our index includes nutritious foods like fruit and nuts and seeds, along with adverse foods like fried foods and high fat meat, which can have opposing impacts on cardiometabolic health. However, in their examination using 2006 CHNS data, Wang et. al also found inconsistent associations between two diet quality measures and CMD odds; finding a negative association between the China Dietary Guideline Index and T2DM, but not with the tailored Alternative Eating Index, although both were negatively associated with abdominal obesity(27). The authors found that neither of the diet quality measures were associated with T2DM in women, and in both sexes neither were associated with high blood pressure or metabolic syndrome (27). It is possible that other factors, in addition to diet urbanization and diet quality, play a role in CMD outcomes amid a nutrition transition, such as macronutrient energy balance, the microbiome, or chronic stress response.
Our findings point to a complex dynamic related to urbanization, diet, and the nutrition transition, leaving more work to do to understand how exactly the nutrition transition leads to adverse cardiometabolic health. Better measurement of the urbanized diet may aid in better understanding of this complex dynamic, and our paper is one step in that direction. Traditional diets differ globally and within China, so there is no single set of factors that capture the nutrition transition. Thus, our paper presents a methodology approach that may be broadly applied to different populations, but likely with adaptation and tailoring to local cultural dietary traditions and regional variations.
A main strength of our study is the use of the large CHNS study sample, which includes extensive covariate data allowing us to adjust models for important confounding variables. Three 24-hour diet recalls allowed for the use of average diet data, which increased reliability of our analyses. In addition, the use of household inventories and questionnaires enabled us to use data gathered from multiple sources, further increasing reliability. The diet index includes variables capturing many aspects of an urbanized diet.
There were some limitations to our study and its results. Our findings are limited to the Chinese population and cannot be extrapolated to other populations, as only CHNS data was used. There is also great diversity within China, so we may not have accurately captured an urban diet for all people in China. In addition, the study only utilized 2015 data, thus preventing longitudinal analysis of changes in diet and health outcomes. As is true with all self-reported data, diet recalls may not be fully representative of each participant’s diet, and even three days of diet assessment may not be enough to capture some episodically consumed foods. Another minor limitation within the CHNS is that the CHNS survey questions did not specifically delineate between type I and type II DM. However, it can be presumed that most, if not all, cases of DM are type II, as type I DM has very low prevalence in China. As mentioned previously, the measure we developed captured the urbanized diet but does not provide insight into diet quality specifically, so further investigation should be done into the quality of an urbanized diet and associations with disease.
The methods used for this project, and the urbanized diet index itself, add to the resources available to study and better understand dietary urbanization. While there are existing measures that capture overall urbanization, we developed a measure that specifically captures dietary urbanization. The methodology presented can be used to further study changes in diet and its impacts in other urbanizing countries.