A new study entitled “Spatial pattern of body mass index among adults in the diabetes study of Northern California (DISTANCE)” identified a clustering factor according to body mass index in adults with diabetes living in Northern California. The study was published in the International Journal of Health Geographics.
Obesity is a significant public health concern, with more than two-thirds of the American population registered as obese and medical related-cost of billions of dollars. While there are genetic factors that can contribute to an increased risk of being overweight or obese, socioeconomic factors can impact the risk for obesity and obesity-related diseases, such as diabetes and heart failure. Notably, an increasing number of reports have suggested that living in deprived regions is associated with increased levels of diabetes incidence: an example in the U.S. was the Moving to Opportunity for Fair Housing program, where by relocating poorer families to fewer deprived areas resulted in a decreased incidence of obese and diabetic adults over the course of ten years.
In this study, the authors hypothesized that adults with diabetes would cluster geographically according to high or low body mass index (BMI), a standard measure to calculate the amount of fat, meaning that adults with high BMI live closer to other high BMI individuals, and vice-versa.
Researchers analyzed 15,854 adults with diabetes enrolled in the Diabetes Study of Northern California (DISTANCE) cohort and quantified their geographical clustering according to BMI. Additionally, they determined which measures can act as predictors of spatial clustering, analyzing demographic and socioeconomic factors (designated as individual factors) and neighborhood deprivation and food environment (designated as contextual factors).
The authors found clustering among diabetic adult individuals with extremely high or low BMI levels across Northern California. Specifically, they observed that BMI clustering was determined more by individual factors (their choices and lifestyle preferences) rather than by contextual factors, with a correlation of 68% and 50%, respectively. Results suggest that additional research should uncover other individual factors that can correlate with health status, therefore being potentially used to identify areas in greater need of clinical intervention. While recent studies observed a spatial clustering according to cardiometabolic factors, the connection between neighborhood characteristics and cardiometabolic risk clustering needs further research, according to the authors.
