A closed-loop system of insulin delivery (also called an artificial pancreas) may improve glycemic outcomes in children with type 1 diabetes.
In a 16-week, multicenter, randomized, open-label, ...parallel-group trial, we assigned, in a 3:1 ratio, children 6 to 13 years of age who had type 1 diabetes to receive treatment with the use of either a closed-loop system of insulin delivery (closed-loop group) or a sensor-augmented insulin pump (control group). The primary outcome was the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter, as measured by continuous glucose monitoring.
A total of 101 children underwent randomization (78 to the closed-loop group and 23 to the control group); the glycated hemoglobin levels at baseline ranged from 5.7 to 10.1%. The mean (±SD) percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter increased from 53±17% at baseline to 67±10% (the mean over 16 weeks of treatment) in the closed-loop group and from 51±16% to 55±13% in the control group (mean adjusted difference, 11 percentage points equivalent to 2.6 hours per day; 95% confidence interval, 7 to 14; P<0.001). In both groups, the median percentage of time that the glucose level was below 70 mg per deciliter was low (1.6% in the closed-loop group and 1.8% in the control group). In the closed-loop group, the median percentage of time that the system was in the closed-loop mode was 93% (interquartile range, 91 to 95). No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either group.
In this 16-week trial involving children with type 1 diabetes, the glucose level was in the target range for a greater percentage of time with the use of a closed-loop system than with the use of a sensor-augmented insulin pump. (Funded by Tandem Diabetes Care and the National Institute of Diabetes and Digestive and Kidney Diseases; ClinicalTrials.gov number, NCT03844789.).
Self-monitoring of blood glucose was described as one of the most important advancements in diabetes management since the invention of insulin in 1920. Recent advances in glucose sensor technology ...for measuring interstitial glucose concentrations have challenged the dominance of glucose meters in diabetes management, while raising questions about the relationships between interstitial and blood glucose levels. This article will review the differences between interstitial and blood glucose and some of the challenges in measuring interstitial glucose levels accurately.
Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions ...depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.
Graphical abstract
Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals
. This could be related to a lack of ...expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10-21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n = 54) or by physicians (physician arm, n = 54). The results for the primary efficacy measure-the percentage of time spent within the target glucose range (70-180 mg dl
(3.9-10.0 mmol l
))-in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P < 1 × 10
). The percentage of readings below 54 mg dl
(<3.0 mmol l
) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers.
Objective To define the demographic and clinical characteristics of children at the onset of type 1 diabetes (T1D), with particular attention to the frequency of diabetic ketoacidosis (DKA). Study ...design The Pediatric Diabetes Consortium enrolled children with new-onset T1D into a common database. For this report, eligible subjects were aged <19 years, had a pH or HCO3 value recorded at diagnosis, and were positive for at least one diabetes-associated autoantibody. Of the 1054 children enrolled, 805 met the inclusion criteria. A pH of <7.3 and/or HCO3 value of <15 mEq/L defined DKA. Data collected included height, weight, hemoglobin A1c, and demographic information (eg, race/ethnicity, health insurance status, parental education, family income). Results The 805 children had a mean age of 9.2 years, 50% were female; 63% were non-Hispanic Caucasian. Overall, 34% of the children presented in DKA, half with moderate or severe DKA (pH <7.2). The risk for DKA was estimated as 54% in children aged <3 years and 33% in those aged ≥3 years ( P = . 006). In multivariate analysis, younger age ( P = . 002), lack of private health insurance ( P < . 001), African-American race ( P = . 01), and no family history of T1D ( P = . 001) were independently predictive of DKA. The mean initial hemoglobin A1c was higher in the children with DKA compared with those without DKA (12.5% ± 1.9% vs 11.1% ± 2.4%; P < . 001). Conclusion The incidence of DKA in children at the onset of T1D remains high, with approximately one-third presenting with DKA and one-sixth with moderate or severe DKA. Increased awareness of T1D in the medical and lay communities is needed to decrease the incidence of this life-threatening complication.
An artificial neural network (ANN) model was developed for simulating water levels at the Sultan Marshes in Turkey. Sultan Marshes is a closed basin wetland located in the semi-arid Central Anatolia ...region of Turkey. It is one of the thirteen Ramsar sites of Turkey and a national park. Water levels at the Sultan Marshes showed strong fluctuations in recent decades due to the changes in climatic and hydrologic conditions. In this study, monthly average water levels were simulated using a multi-layer perceptron type ANN model. The model inputs consisted of climatic data (precipitation, air temperature, evapotranspiration) and hydrologic data (ground water levels, spring flow rates, and previous month water levels) available from 1993 to 2002. 70 % of the data were used for model training and remaining 30 % were used for model testing. Model training was accomplished by using a scaled conjugate gradient backpropagation algorithm. The performance of the model was evaluated by calculating the root mean square error (RMSE) and the coefficient of determination (R ²) between observed and simulated water levels. The sensitivity of the model to input parameters was determined by evaluating the model performance when a single input variable was excluded. It was found that the ANN model can successfully be used for simulating water levels at the Sultan Marshes. The model developed using all input variables provided the best results with two neurons in the hidden layer. The RMSE and R ² of the simulated water levels were 4.0 cm and 96 %, respectively. The sensitivity analysis showed that the model was most sensitive to previous month water levels and ground water levels.
An integrated sensor-augmented pump system has been introduced that interrupts basal insulin infusion for 2 h if patients fail to respond to low-glucose alarms. It has been suggested that such ...interruptions of basal insulin due to falsely low glucose levels detected by sensor could lead to diabetic ketoacidosis. We hypothesized that random suspension of basal insulin for 2 h in the overnight period would not lead to clinically important increases in blood β-hydroxybutyrate levels despite widely varying glucose values prior to the suspension.
Subjects measured blood glucose and blood β-hydroxybutyrate levels using a meter each night at 9:00 p.m., then fasted until the next morning. On control nights, the usual basal rates were continued; on experimental nights, the basal insulin infusion was reprogrammed for a 2-h zero basal rate at random times after 11:30 p.m.
In 17 type 1 diabetic subjects (mean age 24 ± 9 years, diabetes duration 14 ± 11 years, A1C level 7.3 ± 0.5% 56 mmol/mol), blood glucose and blood β-hydroxybutyrate levels were similar at 9:00 p.m. on suspend nights (144 ± 63 mg/dL and 0.09 ± 0.07 mmol/L) and nonsuspend nights (151 ± 65 mg/dL and 0.08 ± 0.06 mmol/L) (P = 0.39 and P = 0.47, respectively). Fasting morning blood glucose levels increased after suspend nights compared with nonsuspend nights (191 ± 68 vs. 141 ± 75 mg/dL, P < 0.0001), and the frequency of fasting hypoglycemia decreased the morning following suspend nights (P < 0.0001). Morning blood β-hydroxybutyrate levels were slightly higher after suspension (0.13 ± 0.14 vs. 0.09 ± 0.11 mmol/L, P = 0.053), but the difference was not clinically important.
Systems that suspend basal insulin for 2 h are safe and do not lead to clinically significant ketonemia even if the blood glucose level is elevated at the time of the suspension.
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