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While our research contributes substantive and methodological innovations to the field there are still gaps in our understanding about the role of the food environment on diet and

consume, and why. Food stores and restaurants offer a wide variety of foods and beverages and the quality of what is offered are not well characterized. Audit studies that characterize the quality of items available within restaurants and stores (e.g., Nutrition Environment

Survey)123,140,177 _{are improving how outlets can be classified regarding the healthiness of their} inventory. In addition, access to scanned purchasing data (e.g., Nielsen Homescan Panel)is a valuable tool to objectively capture changes in purchasing behavior.178 _{However, the pathway} that connects the individuals to the exact point of purchase and later food consumption is lacking. Future research should link individuals in real time and space with the food resources they visit, what they purchase and then consume. Further, information about triggers and decisions about underlying diet behaviors could inform more tailored polices rather than focusing on numbers of different types of food stores and restaurants with a given geographic area. As new technologies and applications develop, future studies may capture the complete path in real time exposures, behaviors and outcomes.

Pathways from the food environment to BMI through diet could differ by age because younger versus older participants may have been exposed to different types of neighborhood food environments due to the circumstances of their life course (e.g., college). The CARDIA study design recruited participants at baseline so there would be equal sample sizes by ages 18- 24 years and 25 to 30 years. In a sensitivity analysis, we took advantage of the age-based

sampling and ran a multi-group analysis by age group (18-24 years versus 25 to 30 years). While the pathways findings were not statistically significantly different by age group there were some differences. The associations between restaurants and diet behaviors appeared to be stronger in both magnitude and significance at baseline for the younger than the older participants. The associations between the convenience stores and diet were stronger in both magnitude and

significance at year 20 for the older than the younger group. Future research should explore how people differ in responses to food environments throughout the life course because there may be windows of time when people are more particularly vulnerable to “obesogenic” food

environments to improve targeted food environment interventions.

Despite the uniqueness of this large racially diverse and prospective cohort, we lacked participants from other minority race/ethnicities that comprise a significant and growing

proportion of the nation’s demographics.179 Further certain race subgroups, such as Hispanics are experiencing faster increases in obesity than non-Hispanic whites.180 Thus, future research should explore pathways from the food environment to BMI through diet in more ethnically diverse populations that better represent the U.S. demographics. In addition, how pathways from neighborhood food environments to BMI through diet operate in rural areas is relatively

unknown and warrants future research.

Food taxation and pricing policies can also influence diet behavior, therefore we need to understand how the food environment influences behaviors in the context of food and agriculture polices. New or changing policies offer researchers opportunities to take advantage of natural experiments to assess pre and post effects. Although more refined analyses might integrate legislative processes with epidemiological cohort data to establish timing of exposures and outcomes.

We hypothesized that visiting a restaurant versus food shopping in a food store are distinct processes but his may not be true. Perhaps decisions about which restaurant to visit can be swayed by nearby food stores and similarly some may decide, for example, to forego food shopping to eat dinner at a restaurant. In addition there is overlap among the diet behaviors we

included the pathways from each type of food resource to each of the latent diet behaviors. Compared to our main findings, the associations between restaurants and diet behaviors were attenuated. Greater numbers of neighborhood fast food restaurants were only associated with greater consumption at year 0 (β=0.23, P<0.001), while greater numbers of sit-down restaurants were associated with lower consumption (year 0: β=-0.27, P<0.001; year 7: β=-0.17, P<0.001) of foods typically purchased from fast food restaurants. At the same time, greater numbers of supermarkets were associated with lower consumption of the fast food (year 0: β=-0.21,

P<0.001) and sit-down (year 0: β=-0.14, P<0.001; year 20: β=-0.09, P<0.001) restaurant-type diets. Conversely, greater numbers of convenience stores were associated with greater

consumption of both the fast food (year 0: β=0.08, P=0.005) and sit-down (year 0: β=0.09,

P<0.001; year 7: β=0.07, P=0.001) restaurant-type diets. Compared to our main findings for food stores, the pathways remained inconsistent when we included pathways from alternative

neighborhood restaurants to influence consuming foods typically offered in food stores. However, greater numbers of fast food restaurants were associated with lower consumption of the supermarket-type diet (year 0: β=-0.25, P=0.001). Living near sit-down restaurants was associated with greater consumption of the supermarket-type diet (year 0: β=0.19, P=0.001) and lower consumption of the convenience store-type diet (year 0: β=-0.11, P<0.001). Future

research should explore how diet behaviors associate with alternative restaurants and food stores. There is limited information about what people actually consume at fast food versus sit- down restaurants and from purchases made at different types food stores. So, there may be other approaches to model diet behaviors that might be more meaningful to individuals. Further, dietary patterns, such as the Dietary Approach to Stop Hypertension diet, may better encompass the consumption of foods we did not include in our model but that are influenced by the food

environment and contribute to weight gain (e.g., alcohol).