The escalating prevalence of overweight among children and adolescents, which tripled between 1980 and 2002 [1,2], is a leading public health problem in the United States [3,4]. Compared to healthy youth, those with diabetes are at higher risk for development of cardiovascular risk factors. Contrary to general perceptions, youth with type 1 diabetes exhibit levels of overweight and obesity comparable to general youth populations [5]. Furthermore, type 2 diabetes has recently emerged in youth, with the vast majority of those affected having extremely high body mass index (BMI) values [5].

To date, most epidemiologic studies exploring the built food environment and health outcomes have focused on BMI, overweight or obesity among adults [6-10] with very few reports on children and adolescents [11-14]. Only one study has been conducted on the impact of the food environment on persons with diabetes [15]. Previous studies have reported poor dietary intake [16] and higher likelihood of cardiovascular risk factors including obesity [5,17] among youth with diabetes, despite an individual-level effort for diabetes management (such as medical nutrition therapy). Furthermore, the findings on lower availability of recommended foods such as whole grains, fruits, vegetables etc. for those with diabetes in low-income minority neighborhoods as an environmental barrier/predictor of meeting dietary recommendations for reducing diabetes complications and maintaining good health [18], suggest some causal link. However, the associations still need to be clarified given the dearth of data among population with diabetes.

One of the major concern of previous research assessing the influence of the built food environment is the characterization of the food environment, which has largely been limited to one environmental attribute at a time [6-8,14]. However, the availability of multiple different food outlet types in the same neighborhood [10], emphasizes the need for evaluation of the influence of these various outlet types. Separate evaluation of these food outlets can lead to false findings when the outlets are clustered in same geographic space. Thus, we aimed to explore the associations of accessibility and availability of supermarkets and fast food outlets with BMI z-score and waist circumference, in a high risk population of youth with diabetes (type 1 and type 2 diabetes) from South Carolina (SC). Distance to nearest food outlets from youth’s residence represented accessibility, and number and density of food outlets around youth’s residence represented availability and accessibility/availability measures, respectively. We evaluated the associations of supermarket and fast food outlet accessibility and availability with each adiposity measure separately and simultaneously.

Findings from our study suggest that increased accessibility/availability of supermarkets may be associated with decreased BMI z-score and waist circumference among youth with diabetes. However, the question of whether fast food outlets, environment considered to serve energy-dense foods, influence adiposity remains inconclusive.

The inverse associations between number and density of supermarkets around residence locations, and adiposity measures were in the expected direction and in agreement with some previous studies [6,9,10,12]. This may be due to the availability of various options/selections of health-promoting foods including fruits/vegetables and low-calorie products in a competitive environment with larger numbers of chain supermarkets, which can promote healthy dietary intake and ultimately health outcomes. However, factors such as individual’s food shopping skills/practices [28], and individual’s purchasing behaviors and social perceptions [29] can equally influence the relationships of food environment with health behaviors and outcomes.

The magnitude of threshold effects we observed with supermarket density quartiles and adiposity measures were very striking. Significantly higher weight and waist circumference were observed for residence locations with a lower supermarket densities compared to the highest supermarket density locations. For instance, for a female child of 10 years of age with height of 54.2 inches and weight of 70.4 pounds, lower supermarket density around residence location was associated with about 2.8–3.2 pounds higher weight, when compared to female child of same age, height and weight with the highest supermarket density around residence location. Similarly, a lower supermarket density around residence location was associated with a 3.5–3.7 centimeter higher waist circumference, when compared to residence location with the highest supermarket density.

The direction and magnitude of associations of supermarket accessibility/availability with both adiposity measures: BMI z-score and waist circumference further support a promising relationship between built food environment and increasing obesity. Previous studies have reported waist circumference as one of the best indicator of abdominal obesity in adults[30] as well as in children and adolescents [31]. In addition, it is also reported as a better predictor of cardiovascular disease risk in children [32] compared to BMI. To our knowledge, only one study among adults has examined the relationship between fast food outlet availability and change in waist circumference [33]. The study found that a high density of fast food outlets was associated with significantly increased waist circumference but only among frequent fast food outlets users.

While our study provided a strong support for the inverse associations of number and density of supermarkets around residence locations with adiposity measures, the associations of number and density of fast food outlets with adiposity measures were not significant. Furthermore, contrary to our hypothesis, we found significantly higher BMI z-score the farther the youth resided from the nearest fast food outlet. This unexpected direction of the association can be attributed to the spatial co-occurrences/clustering of both food outlet types. Our study region showed that almost 78% of supermarkets had one or more fast food outlets within one-half mile of supermarket locations. Spatial co-occurrences of multiple outlets in geographic space has been suggested earlier [10]. Majority of individuals residing far from chain fast food outlets may also reside far from chain supermarkets, particularly in semi-rural and rural settings. In such instance, residents will have to depend upon the local small food outlets for frequent food purchases. Previous studies have reported fewer supermarkets and larger proportions of small stores and convenience stores in rural areas [34,35], which offer limited selection and lower quality products [36,37] including fast food items [38,39], when compared with their urban counterparts. Hence, increased access to these types of local food venues, which are not considered in our study, could have attributed to higher BMI z-score among our youth population even though they resided far from the chain fast food outlets.

Mixed results have been reported on the influence of fast food outlets on adiposity. Previous studies found that the increased availability and accessibility of fast food outlets significantly contributed to increase in BMI, overweight or obesity [9,14,33]. However, other studies found no associations [7,11,23]. Despite extensive evidence linking fast food consumption with high energy and fat intake, nutrient-poor food, and increased overweight and obesity[40]; these mixed results on the influence of fast food outlets, can be attributed to a number of factors . Researchers have used various geographic scales of analysis (from state to Census block group [9,12,33,41,42]) for food environment measures. Furthermore, studies varied in terms of the anchoring point where the fast food usage was measured. Studies among children and adolescents used home [11,23] and/or school [14,43] locations. Studies among adults mostly used home [7,9,33]; only one study used both home and work [7] locations. The one-location approach can be unrealistic, given the possibility of use of a fast food outlet at particular points in time and space when a person is in need of something to eat [7]. Home, therefore, as used in our study, may represent only one of the many locations of fast food usage, which could explain the lack of significant associations of fast food availability with adiposity. Particularly, among children and youth, consideration of both home and school fast food environments are important since both can equally influence the eating behavior and hence adiposity.

Our study has several limitations. First, the addresses are the contact addresses of the youth and may not represent the residential location. Similar to the majority of built food environment studies, the food environment data used in our study was collected several years after the individual-level data were collected. It is likely that some changes occurred in food environments during the study period, however, these changes would most likely be occurring independently of adiposity of our study population and thus lead to non-differential misclassification. One of the major concerns in previous epidemiologic studies exploring the impact of food environment has been the validity of the food outlets data from secondary data sources [44]. However, our recent validation work in SC showed better sensitivity and positive predicted values for both supermarkets/grocery stores and limited service restaurants, when the combination of SCDHEC and InfoUSA datasets were used [24]. Hence, consideration of both data sources in our study would have minimized the count error. Furthermore, for error in the food outlet database to bias our results, it would have to be correlated with individual’s adiposity, which is highly unlikely. Our study considered only franchised grocery stores and franchised fast food outlets and did not include other local non-chain outlets because of high chances of misclassification (e.g. convenience stores providing food high in sugar, salt and fat were classified as small local grocery stores by secondary data sources).

Our study contributes to the literature in several ways. First, we quantified the accessibility and availability of food outlets at individual-level, an approach that has been described as ‘cutting edge’ in built food environment study [45]. Second, we included youth from the entire state ranging from urban to isolated rural areas. Third, we explored the impact of food environment on adiposity among youth with diabetes by considering two different outlets types that provide different food purchase options/selections. Only a few studies have explored the associations of various types of food environments with health outcomes among adults [9,10] and among children and youth populations [11,43]. This two-fold approach can be an important aspect to consider in SC and other states which lack specific land-use zoning and are typified by clustering of retail locations around arterial roads with high traffic volume [46]. Fourth, our study population included wide age range and large proportion of African Americans. Finally, we included three accessibility and availability measures that allowed us to capture different dimensions of the food environment, including evaluation of immediate proximity, variety and diversity in order to explore their associations with adiposity.

In summary, our study suggests that built food environment may be an important contextual factor that significantly influences health behaviors and outcomes among youth with diabetes above and beyond the individual-level risk factors. In particular, higher number and density of supermarkets around residence location may lower excess weight and waist circumference gain among children and youth with diabetes, even in presence of outlets providing easy fast food options. Hence, efforts to increase availability of and awareness about healthy foods can have potential health implications. A recent state indicator report on fruits and vegetables published by the Centers for Disease Control and Prevention also provides support for policy and environmental strategies to improve fruit and vegetable availability and consumption [47]. Particularly, efforts to promote farmers markets, and small grocery stores and convenience stores offer fruits/vegetables and other healthy options can be a better solutions in rural areas, which have fewer chain supermarkets [34,35].

The relationship of fast food outlet with BMI z-score and waist circumference, however, remains inconclusive. Contrary to our expectation, higher BMI z-score was observed farther an individual resided from the nearest chain fast food outlet. This association may have been a result of lower access to health-promoting foods and increased access to lower quality products including fast food items, particularly in rural settings. Hence, consideration of these small food venues in future studies may help capture the total food environment exposures and assess their influence on health behavior and outcomes.

The relationships of food environment and health behaviors and outcomes can also be affected by factors such as individual’s food shopping skills/practices [28] and individual’s purchasing behaviors, perceptions of the availability and prices of foods, and social perceptions [29]. However, this link has not been well documented. Hence, future studies should explore individual, social as well as environment factors to provide a more comprehensive understanding of the influences of food environment on health outcomes.

The authors declare that they have no competing interests.

APL designed the study, collected data, performed data management and analysis, interpreted data and drafted the manuscript. RP contributed to the conception of the study, interpretation of data, and critically revised and edited the manuscript. DEP contributed to the conception of the study, and critically revised and edited the manuscript. MB contributed to the conception of the study, data analysis, interpretation of the data, and critically revised and edited the manuscript. EJM contributed to the conception of the study, interpretation of the data, and critically revised and edited the manuscript. ADL contributed to the design of the study, interpretation of the data and critically revised and edited the manuscript. All authors read and approved the final version of the manuscript.