Objective: The purpose of this study was to investigate whether or not adding a fixed preprandial dose of inhaled insulin to a fully automated closed loop artificial pancreas would improve the postprandial glucose control without adding an increased risk of hypoglycemia. Research Design and Methods: Nine subjects with T1DM were recruited for the study. The patients were on closed-loop control for 24 hours starting around 4:30 pm. Mixed meals (~50 g CHO) were given at 6:30 pm and 7:00 am the following day. For the treatment group each meal was preceded by the inhalation of one 10 U dose of Technosphere Insulin (TI). Subcutaneous insulin delivery was controlled by a zone model predictive control algorithm (zone-MPC). At 11:00 am, the patient exercised for 30 ± 5 minutes at 50% of predicted heart rate reserve. Results: The use of TI resulted in increasing the median percentage time in range (70-180 mg/dl, BG) during the 5-hour postprandial period by 21.6% (81.6% and 60% in the with/without TI cases, respectively, P = .06) and reducing the median postprandial glucose peak by 33 mg/dl (172 mg/dl and 205 mg/dl in the with and without TI cases, respectively, P = .004). The median percentage time in range 80-140 mg/dl during the entire study period was 67.5% as compared to percentage time in range without the use of TI of 55.2% (P = .03). Conclusions: Adding preprandial TI (See video supplement) to an automated closed-loop AP system resulted in superior postprandial control as demonstrated by lower postprandial glucose exposure without addition hypoglycemia.

This article reviews the concept of using the proper methods for the proper task, in this case measuring dose-to-dose accuracy of continuous subcutaneous insulin infusion pumps.

The relationship between what and how individuals eat and their overall and long-term health is non-controversial. However, for decades, food and nutrition discussions have often been highly medicalized. Given the significant impact of poor nutrition on health, broader discussions about food should be integrated into routine patient history taking. We advocate for an expansion of the current, standard approach to patient history taking in order to include questions regarding patients’ ‘foodlife’ (total relationship to food) as a screening and baseline assessment tool for referrals. We propose that healthcare providers: (1) routinely engage with patients about their relationship to food, and (2) recognize that such dialogues extend beyond nutrition and lifestyle questions. Mirroring other recent revisions to medical history taking—such as exploring biopsychosocial risks—questions about food relationships and motivators of eating may be essential for optimal patient assessment and referrals. We draw on the novel tools of ‘foodlife’ ethnography (developed by co-author ethnographer J.J.L., and further refined in collaboration with the co-authors who contributed their clinical experiences as a former primary care physician (D.M.E.), registered dietitian (J.W.M.), and diabetologist (H.Z.)) to model a set of baseline questions for inclusion in routine clinical settings. Importantly, this broader cultural approach seeks to augment and enhance current food intake discussions used by registered dietitian nutritionists, endocrinologists, internists, and medical primary care providers for better baseline assessments and referrals. By bringing the significance of food into the domain of routine medical interviewing practices by a range of health professionals, we theorize that this approach can set a strong foundation of trust between patients and healthcare professionals, underscoring food’s vital role in patient-centered care.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

Glucose data regarding extreme elite performances in athletes without diabetes remains limited. The purpose is to characterize continuous glucose monitoring (CGM) responses in elite athletes across distinct high-performance contexts. This descriptive case series includes three separate elite athletes who used a CGM during their respective sporting events. The first is an ultra-endurance relay cycling world-record performance (Race Across the West, RAW), the second is a continuous high-intensity Everesting Challenge cycling record attempt, and the third is a maximal constant-weight no-fins breath-hold depth dive performed in international competition. Glycemic outcomes, as measured by CGM, included mean, maximum, and minimum glucose, glucose standard deviation (SD), and the percentage of time in tight glucose range (TITR: 70–140 mg/dL; 3.9–7.8 mmol/L), time below range (TBR: <70 mg/dL; 140 mg/dL; >7.8 mmol/L). Other performance data, including peak power, heart rate, and lactate, are also provided where available. During the RAW challenge lasting 44 h and 20 min, mean glucose was 91 ± 23.2 mg/dL (mean ± SD) with 9.15% TBR and 35.58% TITR during cycling and 115 ± 24.7 mg/dL with 9.11% TBR and 43.16% TITR during resting periods. In contrast, the Everesting Challenge cycling record attempt demonstrated a persistently elevated glucose profile (160 ± 5.7 mg/dL), minimal variability (CV 3.5%), and 100% TAR140. Following the maximal breath-hold depth dive, interstitial glucose was 100% TAR140 during recovery (187 ± 18.5 mg/dL), alongside marked elevations in blood lactate concentrations (peak 13.4 mmol/L). The series of case studies demonstrate that substantial deviations from traditional euglycemic ranges are common during elite performance in athletes without diabetes. Interpretation of CGM data in athletic settings should therefore be performance- and context-specific rather than based on clinical glycemic thresholds.