Highlightsd 20 inpatient adults received ultra-processed and unprocessed diets for 14 days each d Diets were matched for presented calories, sugar, fat, fiber, and macronutrients d Ad libitum intake was 500 kcal/day more on the ultraprocessed versus unprocessed diet d Body weight changes were highly correlated with diet differences in energy intake

OBJECTIVETo assess the risk factors for the presence and severity of obstructive sleep apnea (OSA) among obese patients with type 2 diabetes.RESEARCH DESIGN AND METHODSUnattended polysomnography was performed in 306 participants.RESULTSOver 86% of participants had OSA with an apnea-hypopnea index (AHI) ≥5 events/h. The mean AHI was 20.5 ± 16.8 events/h. A total of 30.5% of the participants had moderate OSA (15 ≤ AHI <30), and 22.6% had severe OSA (AHI ≥30). Waist circumference (odds ratio 1.1; 95% CI 1.0–1.1; P = 0.03) was significantly related to the presence of OSA. Severe OSA was most likely in individuals with a higher BMI (odds ratio 1.1; 95% CI 1.0–1.2; P = 0.03).CONCLUSIONSPhysicians should be particularly cognizant of the likelihood of OSA in obese patients with type 2 diabetes, especially among individuals with higher waist circumference and BMI.

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Research shows that community socioeconomic status (SES) predicts, based on food service types available, whether a population has access to healthy food. It is not known, however, if a relationship exists between SES and risk for foodborne illness (FBI) at the community level. Geographic information systems (GIS) give researchers the ability to pinpoint health indicators to specific geographic locations and detect resulting environmental gradients. It has been used extensively to characterize the food environment, with respect to access to healthy foods. This research investigated the utility of GIS in determining whether community SES and/or demographics relate to access to safe food, as measured by food service critical health code violations (CHV) as a proxy for risk for FBI. Health inspection records documenting CHV for 10,859 food service facilities collected between 2005 and 2008 in Philadelphia, PA, were accessed. Using an overlay analysis through GIS, CHV were plotted over census tracts of the corresponding area. Census tracts (n = 368) were categorized into quintiles, based on poverty level. Overall, food service facilities in higher poverty areas had a greater number of facilities (with at least one CHV) and had more frequent inspections than facilities in lower poverty areas. The facilities in lower poverty areas, however, had a higher average number of CHV per inspection. Analysis of CHV rates in census tracts with high concentrations of minority populations found Hispanic facilities had more CHV than other demographics, and Hispanic and African American facilities had fewer days between inspections. This research demonstrates the potential for utilization of GIS mapping for tracking risks for FBI. Conversely, it sheds light on the subjective nature of health inspections, and indicates that underlying factors might be affecting inspection frequency and identification of CHV, such that CHV might not be a true proxy for risk for FBI.