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Optimizing postprandial glucose control in persons with type 1 diabetes (T1D) is challenging. We hypothesized that in free-living individuals, meal composition (high and low glycemic index [HGI and LGI], high and low fat [HF and LF]) may impact insulin requirements. Adults ( N = 25) with T1D using open-loop insulin and continuous glucose monitoring were provided a meal-tagging app and prepackaged meals with defined macronutrient content. Data from 463 meals were analyzed. LGI meals required significantly more insulin than HGI meals ( P = 0.01). Furthermore, the mean (±standard deviation) carbohydrate-to-insulin ratio (CIR) was significantly different overall among the LGI-LF (5.5 ± 3.4), LGI-HF (4.5 ± 3.8), HGI-LF (7.6 ± 5.1), and HGI-HF (8.7 ± 5.8) meals ( P = 0.001). The risk of nocturnal hypoglycemia is associated with daytime hypoglycemia and amount of insulin administered prior to the evening and exercise. This exploratory study designed to examine the impact of different meal types on insulin dosing requirements in free-living adults with T1D emphasizes the need for individualized adjustment of the CIR depending on meal composition.

Objective: This study used connected pen to determine missed bolus dose (MBD) frequency during masked and unmasked continuous glucose monitoring (CGM) periods and examined its link with time-in-range (TIR), time-above-range (TAR), time-below-range (TBR), and key participant characteristics in people with diabetes. Methods: This was a 12-week, single-arm, exploratory, two-period study for people with type 1 diabetes (T1D) or type 2 diabetes (T2D). The primary objective was to estimate the average number of MBD during masked and real-time CGM use. The secondary objective was to estimate the average percent TIR and its relationship to MBD. An exploratory objective was to investigate the participant characteristics that were associated with MBD. Data were analyzed for differences in MBD by diabetes type and other participant characteristics, by CGM period, and by hypoglycemic fear scores. Results: Participants ( n  = 64; T1D, n  = 38; T2D, n  = 26) were 48 ± 11.9 years old and 44% were female. From the masked to the unmasked period, MBD, %TAR, %TBR, and glycated hemoglobin decreased significantly (0.74 MBD/day to 0.62 MBD/day, P  = 0.008; 53.6%-48.1%, P  = 0.004; 4.49%-2.93%, P  < 0.001; mean 8.8%-8.4%, P  < 0.001, respectively), while %TIR increased significantly (41.9%-49.0%, P  < 0.001). MBD/day was negatively associated with TIR ( P  = 0.016) and positively associated with TAR ( P  = 0.015) for T1D and positively associated with TBR ( P  = 0.024) for T2D in the masked period only. MBD was significantly associated with fear of hypoglycemia for T2D, but not T1D. Conclusions: MBD is associated with reduced TIR when CGM is masked and tailored therapeutic approaches are needed for T1D and T2D populations.

Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

The advent of connected insulin pens will generate an avalanche of digital insulin data, especially in the context of prandial- and multiple daily injection-insulin regimens. There is a need for the diabetes community to develop standards for such data, analogous to what has been achieved using the ambulatory glucose profile and associated metrics for glucose, permitting harmonization of data reporting for multiple devices and facilitating integration of glucose, insulin, food intake, and physical activity data. Several studies have estimated the timing of meals by analyses of glucose excursions but using diverse criteria. There is need for uniform criteria for multiple types of insulin boluses, including premeal, perimeal, delayed, missed, and correction boluses to facilitate research studies and patient care. This article contains a first preliminary proposal for standards regarding reporting of insulin dosing data. Clinical usage of these reports will require sensitive communication between health care providers and patients.

Background: Automated insulin delivery (AID) systems have demonstrated improvements in time-in-range (TIR, blood glucose 70-180 mg/dL) without increasing hypoglycemia. Testing a closed-loop system in an inpatient environment with supervised challenges allows for initial evaluation of performance and safety of the system. Methods: Adults with type 1 diabetes (T1D) were enrolled into two similar studies ( n  = 10 per study), with 3-day inpatient analysis periods. Participants tested a Lilly hybrid closed-loop (HCL) system comprising an investigational insulin pump, insulin lispro, a pump-embedded model predictive control algorithm, a continuous glucose monitor (CGM), and an external dedicated controller. Each protocol included meal-related and exercise challenges to simulate real-world diabetes self-management errors. Only study staff interacted with the HCL system. Performance was assessed using standard CGM metrics overall and within prespecified periods. Results: Participants (25% male) had mean ± standard deviation (SD) age 44.7 ± 14.2 years, T1D duration 30.2 ± 11.1 years, A1C 7.2% ± 0.8%, and insulin usage 0.53 ± 0.21 U/(kg·day). Percentage TIR 70-180 mg/dL (mean ± SD) was 81.2 ± 8.4 overall, 85.2 ± 8.1 outside of challenge periods, 97.3 ± 5.3 during the nocturnal periods, and 74.5 ± 16.2 for the postprandial periods. During challenge periods, percentage TIR for the overbolus challenge was 65.4 ± 29.2 and that for the delayed bolus challenge was 57.1 ± 25.1. No adverse events (AEs), serious AEs, or unanticipated adverse device events occurred while participants were using the HCL system. Conclusions: In participants with T1D, Lilly AID system demonstrated expected algorithm performance and safety with satisfactory glycemic outcomes overall and in response to simulated diabetes management challenges. Additional studies in less supervised conditions and with broader patient populations are warranted. ClinicalTrials.gov Registration number NCT03743285, NCT03849612.

Background: Traditionally, insulin bolus calculations for managing postprandial glucose levels in individuals with type 1 diabetes rely solely on the carbohydrate content of a meal. However, recent studies have reported that other macronutrients in a meal can alter the insulin required for good postprandial control. Specifically, studies have shown that high-fat (HF) meals require more insulin than low-fat (LF) meals with identical carbohydrate content. Our objective was to assess the mechanisms underlying the higher insulin requirement observed in one of these studies., Materials and Methods: We used a combination of previously validated metabolic models to fit data from a study comparing HF and LF dinners with identical carbohydrate content in seven subjects with type 1 diabetes. For each subject and dinner type, we estimated the model parameters representing the time of peak meal-glucose appearance (τ(m)), insulin sensitivity (S(I)), the net hepatic glucose balance, and the glucose effect at zero insulin in four time windows (dinner, early night, late night, and breakfast) and assessed the differences in model parameters via paired Wilcoxon signed-rank tests., Results: During the HF meal, the τ(m) was significantly delayed (mean and standard error [SE]: 102 [14] min vs. 71 [4] min; P = 0.02), and S(I) was significantly lower (7.25 × 10(-4) [1.29 × 10(-4)] mL/μU/min vs. 8.72 × 10(-4) [1.08 × 10(-4)] mL/μU/min; P = 0.02)., Conclusions: In addition to considering the putative delay in gastric emptying associated with HF meals, we suggest that clinicians reviewing patient records consider that the fat content of these meals may alter S(I).

In recent years continuous subcutaneous insulin infusion pumps have become widely adopted in many parts of the world in the treatment of type 1 diabetes in adults. A comprehensive summary of all aspects of pump therapy is beyond the scope of this article, and in this review we will focus on several practical issues that in our experience are of clinical importance in the care of patients using insulin pumps. These include: benefits and risks of pump therapy, including the use of pumps to limit hypoglycemia; individual patient considerations in choosing between pump therapy and multiple daily injections; common pump-specific etiologies of erratic glucose control, including routine clinical practices that can assist with the detection of these problems; and the use of different pump bolus types for prandial insulin coverage.

Background: The objective of the study was to evaluate the clinical effectiveness and safety of a device that combines an insulin pump with real-time continuous glucose monitoring (CGM), compared to using an insulin pump with standard blood glucose monitoring systems., Methods: This 6-month, randomized, multicenter, treat-to-target study enrolled 146 subjects treated with continuous subcutaneous insulin infusion between the ages of 12 and 72 years with type 1 diabetes and initial A1C levels of >or=7.5%. Subjects were randomized to pump therapy with real-time CGM (sensor group [SG]) or to pump therapy and self-monitoring of blood glucose only (control group [CG]). Clinical effectiveness and safety were evaluated., Results: A1C levels decreased (P<0.001) from baseline (8.44+/-0.70%) in both groups (SG, -0.71+/-0.71%; CG, -0.56+/-0.072%); however, between-group differences did not achieve significance. SG subjects showed no change in mean hypoglycemia area under the curve (AUC), whereas CG subjects showed an increase (P=0.001) in hypoglycemia AUC during the blinded periods of the study. The between-group difference in hypoglycemia AUC was significant (P<0.0002). Greater than 60% sensor utilization was associated with A1C reduction (P=0.0456). Fourteen severe hypoglycemic events occurred (11 in the SG group and three in the CG group, P=0.04)., Conclusions: A1C reduction was no different between the two groups. Subjects in the CG group had increased hypoglycemia AUC and number of events during blinded CGM use; however, there was no increase in hypoglycemia AUC or number of events in the SG group. Subjects with greater sensor utilization showed a greater improvement in A1C levels.

Continuous glucose monitoring (CGM) is an evolving technology poised to redefine current concepts of glycemic control and optimal diabetes management. To date, there are few randomized studies examining how to most effectively use this new tool. Therefore, a group of eight diabetes specialists heard presentations on continuous glucose sensor technology and then discussed their experience with CGM in order to identify fundamental considerations, objectives, and methods for applying this technology in clinical practice. The group concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia. The need for such progress is indicated by the growing prevalence of inadequately treated hyperglycemia. Coordinating financial and educational resources and developing clear protocols for using glucose sensor technology are urgent priorities in promoting wide adoption of CGM by patients and health care providers. Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices.