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Objective: We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D).Research Design and Methods: As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural.Results: Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways.Conclusions: Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice.We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high‐risk agent (insulin or insulin secretagogue).The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five‐year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP‐1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT‐2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), “other” ([aOR] 0.72) or “unknown” ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98).We found an encouraging trend toward less use of high‐risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population. [ABSTRACT FROM AUTHOR]

Large-scale longitudinal studies evaluating influences of the built environment on risk for type 2 diabetes (T2D) are scarce, and findings have been inconsistent. To evaluate whether land use environment (LUE), a proxy of neighborhood walkability, is associated with T2D risk across different US community types, and to assess whether the association is modified by food environment. The Veteran's Administration Diabetes Risk (VADR) study is a retrospective cohort of diabetes-free US veteran patients enrolled in VA primary care facilities nationwide from January 1, 2008, to December 31, 2016, and followed longitudinally through December 31, 2018. A total of 4,096,629 patients had baseline addresses available in electronic health records that were geocoded and assigned a census tract-level LUE score. LUE scores were divided into quartiles, where a higher score indicated higher neighborhood walkability levels. New diagnoses for T2D were identified using a published computable phenotype. Adjusted time-to-event analyses using piecewise exponential models were fit within four strata of community types (higher-density urban, lower-density urban, suburban/small town, and rural). We also evaluated effect modification by tract-level food environment measures within each stratum. In adjusted analyses, higher LUE had a protective effect on T2D risk in rural and suburban/small town communities (linear quartile trend test p-value <0.001). However, in lower density urban communities, higher LUE increased T2D risk (linear quartile trend test p-value <0.001) and no association was found in higher density urban communities (linear quartile trend test p-value = 0.317). Particularly strong protective effects were observed for veterans living in suburban/small towns with more supermarkets and more walkable spaces (p-interaction = 0.001). Among veterans, LUE may influence T2D risk, particularly in rural and suburban communities. Food environment may modify the association between LUE and T2D. [ABSTRACT FROM AUTHOR]

Exposure to particulate matter ≤2.5 μm in diameter (PM 2.5) and ozone (O 3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM 2.5 and O 3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment. Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003–2016 (REGARDS) and 2008–2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM 2.5 and O 3. In two pollutant models, associations of PM 2.5 , and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM 2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type. Associations between PM 2.5 , O 3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations. • PM 2.5 and O 3 were minimally associated with new onset type 2 diabetes (T2D) in three large study samples in the United States. • Results did not substantially differ between single and two pollutant models. • Associations of PM 2.5 and O 3 with new onset T2D were impacted by adjustment for neighborhood socio-economic disadvantage. [ABSTRACT FROM AUTHOR]