Regional variation in type 2 diabetes: evidence from 137 820 adults on the role of neighbourhood body mass index

Using Structural Equation Modeling to Untangle Pathways of Risk Factors Associated with Incident Type 2 Diabetes: the Lifelines Cohort Study

Risk factors for type 2 diabetes are multifaceted and interrelated. Unraveling the complex pathways of modifiable risk factors related to incident type 2 diabetes will help prioritize prevention targets. The current analysis extended a previously proposed conceptual model by Bardenheier et al. (Diabetes Care, 36(9), 2655–2662, 2013) on prediabetes with a cross-sectional design. The model described the pathways of four aspects of modifiable risk factors in relation to incident type 2 diabetes, including socioeconomic status (income and education); lifestyle behaviors (diet quality, physical activity, TV watching, smoking, risk drinking, and unhealthy sleep duration); clinical markers (HDL-cholesterol, triglycerides, BMI, and waist circumference); and blood pressure. We performed structural equation modeling to test this conceptual model using a prospective population-based sample of 68,649 participants (35–80 years) from the Lifelines cohort study. During a median follow-up of 41 months, 1124 new cases of type 2 diabetes were identified (incidence 1.6%). The best-fitting model indicated that among all modifiable risk factors included, waist circumference had the biggest direct effect on type 2 diabetes (standardized β-coefficient 0.214), followed by HDL-cholesterol (standardized β-coefficient − 0.134). Less TV watching and more physical activity were found to play an important role in improving clinical markers that were directly associated with type 2 diabetes. Education had the biggest positive effects on all lifestyle behaviors except for unhealthy sleep duration. Our analysis provides evidence to support that structural equation modeling enables a holistic assessment of the interplay of type 2 diabetes risk factors, which not only allows the estimation of their total effects but also prioritization of prevention targets. Regarding the current guideline for diabetes prevention, waist management in addition to BMI control (clinical level), as well as less TV watching in addition to more physical activity (behavioral level), may provide additional public health benefits. Better education would be the main societal goal for the prevention of type 2 diabetes.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus (DM) is a growing epidemic with global proportions. It is estimated that in 2019, 463 million adults aged 20-79 years were living with DM. The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades, which would have major implications for healthcare expenditures, particularly in developing countries. Hence, new conceptual and methodological approaches to tackle the epidemic are long overdue. Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus. The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases. In this review, we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM. We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM. Finally, we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania

OBJECTIVES

To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.

DESIGN

Nested case-control study within the open dynamic cohort of health system patients.

SETTING

Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.

PARTICIPANTS AND ANALYSIS

We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.

RESULTS

Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.

CONCLUSIONS

Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.