The primary objective of this trial was to evaluate the feasibility, safety, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system vs predictive low glucose ...suspension (PLGS) alone in optimizing overnight glucose control in children 6 to 14 years old.
Twenty-eight participants 6 to 14 years old with T1D duration ≥1 year with daily insulin therapy ≥12 months and on insulin pump therapy for ≥6 months were randomized per night into PHHM mode or PLGS-only mode for 42 nights. The primary outcome was percentage of time in sensor-measured range 70 to 180 mg/dL in the overnight period.
The addition of automated insulin delivery with PHHM increased time in target range (70-180 mg/dL) from 66 ± 11% during PLGS nights to 76 ± 9% during PHHM nights (P<.001), without increasing hypoglycemia as measured by time below various thresholds. Average morning blood glucose improved from 176 ± 28 mg/dL following PLGS nights to 154 ± 19 mg/dL following PHHM nights (P<.001).
The PHHM system was effective in optimizing overnight glycemic control, significantly increasing time in range, lowering mean glucose, and decreasing glycemic variability compared to PLGS alone in children 6 to 14 years old.
To assess the effect of overnight insulin pump suspension in an automated predictive low glucose suspend system on morning blood glucose and ketone levels in an attempt to determine whether routine ...measurement of ketone levels is useful when a closed-loop system that suspends insulin delivery overnight is being used.
Data from an in-home randomized trial of 45 individuals with type 1 diabetes (age range 15-45 years) were analyzed, evaluating an automated predictive low glucose pump suspension system in which blood glucose, blood ketone, and urine ketone levels were measured on 1,954 mornings.
One or more pump suspensions occurred during 744 of the 977 intervention nights (76%). The morning blood ketone level was ≥0.6 mmol/L after 11 of the 744 nights (1.5%) during which a pump suspension occurred and 2 of the 233 nights (0.9%) during which there was no suspension compared with 11 of 977 control nights (1.1%). The morning blood ketone level was ≥0.6 mmol/L after only 2 of 159 nights (1.3%) with a pump suspension exceeding 2 h. Morning fasting blood glucose level was not a good predictor of the presence of blood ketones.
Routine measurement of blood or urine ketones during use of an automated pump suspension system using continuous glucose monitoring, whether threshold based or predictive, is not necessary. Recommendations for checking ketone levels should be no different when a patient is using a system with automated insulin suspension than it is for conventional diabetes self-management.
Aims
To evaluate the efficacy and safety of ultra‐rapid lispro (URLi) versus lispro in a paediatric population with type 1 diabetes (T1D) in a Phase 3, treat‐to‐target study.
Materials and Methods
...After a 4‐week lead‐in to optimize basal insulin, participants were randomized to double‐blind URLi (n = 280) or lispro (n = 298) injected 0 to 2 minutes prior to meals (mealtime), or open‐label URLi (n = 138) injected up to 20 minutes after start of meals (postmeal). Participants remained on pre‐study basal insulin (degludec, detemir or glargine). The primary endpoint was glycated haemoglobin (HbA1c) change from baseline after 26 weeks (noninferiority margin 4.4 mmol/mol 0.4%).
Results
Both mealtime and postmeal URLi demonstrated noninferiority to lispro for HbA1c: estimated treatment difference (ETD) for mealtime URLi −0.23 mmol/mol (95% confidence interval CI −1.84, 1.39) and postmeal URLi −0.17 mmol/mol (95% CI −2.15, 1.81). Mealtime URLi reduced 1‐hour postprandial glucose (PPG) daily mean (P = 0.001) and premeal to 1 hour postmeal PPG excursion daily mean (P < 0.001) versus lispro. The rate and incidence of severe, nocturnal or documented hypoglycaemia (<3.0 mmol/L 54 mg/dL) were similar for all treatments. With mealtime URLi versus lispro, the rate of postdose hypoglycaemia (<3.0 mmol/L) was higher at ≤2 hours (P = 0.034). The incidence of treatment‐emergent adverse events was similar for all treatments. More participants reported an injection site reaction with mealtime URLi (7.9%) versus postmeal URLi (2.9%) and lispro (2.7%).
Conclusions
In children and adolescents with T1D, URLi demonstrated good glycaemic control, and noninferiority to lispro in HbA1c change for mealtime and postmeal URLi. When dosed at the beginning of meals, URLi reduced 1‐hour PPG and PPG excursions versus lispro.
Continuous glucose monitors (CGMs) are an integral part of care for youth with type 1 diabetes (T1D) though lack FDA labeling for inpatient use. While some adult data on CGM use in inpatient settings ...is available, pediatric data are minimal.
This retrospective chart review evaluated the accuracy of Dexcom G6 CGM versus point of care (POC, Nova Biomedical StatStrip MARD 6%)) blood glucose values from pediatric inpatient encounters. Blood glucose data, diagnosis codes, and initial labs were collected from the medical record. CGM values were obtained from Dexcom Clarity CSV files.
Paired glucose values (N=1191) from 83 patients with T1D (median age 12 yrs, 54% male, 69% non-Hispanic White) were used to calculate mean absolute relative difference (MARD) and Clarke Error Grid. Data from DKA admissions (N=665) had a MARD of 11.1% with 97.8% of values within A&B zones, compared to 11.4% and 98.5% for non-DKA admissions (N=526). Values from severe DKA admissions (N= 307) (pH <7.15 and/or bicarbonate <5 mmol/L) had a lower MARD compared to non-severe admissions (N=358) (8.4% vs 13.4%, p=0.01).
In summary, CGM accuracy is comparable between DKA and non-DKA admissions. The accuracy of CGMs, even in severe DKA, suggests potential usability during pediatric hospital encounters. Further analysis will differentiate POC versus lab glucose and the effect of medications, including IV insulin infusions.
Disclosure
L.A.Waterman: None. L.Pyle: None. L.Towers: None. E.Jost: Other Relationship; Tandem Diabetes Care, Inc. A.J.Karami: None. C.Berget: Consultant; Insulet Corporation, Dexcom, Inc., Other Relationship; Tandem Diabetes Care, Inc. G.P.Forlenza: Advisory Panel; Medtronic, Consultant; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc., Lilly Diabetes, Research Support; Medtronic, Abbott, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Wadwa: Consultant; Eli Lilly and Company, Other Relationship; Dexcom, Inc., Eli Lilly and Company, Research Support; Dexcom, Inc., Eli Lilly and Company, Beta Bionics, Inc., Tandem Diabetes Care, Inc. E.C.Cobry: None.
Funding
National Institutes of Health (5T32DK063687)
The objective of this study was to assess the safety and performance of the Omnipod
personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ≥6 years with type ...1 diabetes (T1D) under free-living conditions using an investigational device.
A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6-65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL.
Participants were 11 adults, 10 adolescents, and 15 children aged (mean ± standard deviation) 28.8 ± 7.9, 14.3 ± 1.3, and 9.9 ± 1.0 years, respectively. Percentage time ≥250 mg/dL during HCL was 4.5% ± 4.2%, 3.5% ± 5.0%, and 8.6% ± 8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (
= 0.1,
= 0.02, and
= 0.03). Percentage time <70 mg/dL during HCL was 1.9% ± 1.3%, 2.5% ± 2.0%, and 2.2% ± 1.9%, a statistically significant decrease in adults when compared to ST (
= 0.005,
= 0.3, and
= 0.3). Percentage time 70-180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% ± 7.5% vs. ST 68.0% ± 15.6% for adults (
= 0.08), HCL 79.0% ± 12.6% vs. ST 60.6% ± 13.4% for adolescents (
= 0.01), and HCL 69.2% ± 13.5% vs. ST 54.9% ± 12.9% for children (
= 0.003).
The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ≥6 years to adults with T1D under supervised free-living conditions with challenges, including daily physical activity and unrestricted meals.
Abstract
Background
Rebound hyperglycemia may occur following glucagon treatment for severe hypoglycemia. We assessed rebound hyperglycemia occurrence after nasal glucagon (NG) or injectable glucagon ...(IG) administration in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D).
Methods
This was a pooled analysis of 3 multicenter, randomized, open-label studies (NCT03339453, NCT03421379, NCT01994746) in patients ≥18 years with T1D or T2D with induced hypoglycemia. Proportions of patients achieving treatment success blood glucose (BG) increase to ≥70 mg/dL or increase of ≥20 mg/dL from nadir within 15 and 30 minutes; BG ≥70 mg/dL within 15 minutes; in-range BG (70-180 mg/dL) 1 to 2 and 1 to 4 hours postdose; and BG >180 mg/dL 1 to 2 and 1 to 4 hours postdose were compared. Incremental area under curve (iAUC) of BG >180 mg/dL and area under curve (AUC) of observed BG values postdose were analyzed. Safety was assessed in all studies.
Results
Higher proportions of patients had in-range BG with NG vs IG (1-2 hours: P = .0047; 1-4 hours: P = .0034). Lower proportions of patients had at least 1 BG value >180 mg/dL with NG vs IG (1-2 hours: P = .0034; 1-4 hours: P = .0068). iAUC and AUC were lower with NG vs IG (P = .025 and P < .0001). As expected, similar proportions of patients receiving NG or IG achieved treatment success at 15 and 30 minutes (97-100%). Most patients had BG ≥70 mg/dL within 15 minutes (93-96%). The safety profile was consistent with previous studies.
Conclusion
This study demonstrated lower rebound hyperglycemia risk after NG treatment compared with IG.
Clinical Trial Registration
NCT03421379, NCT03339453, NCT01994746
Aims
To understand morning biopsychosocial factors that predict glycemia, adherence, and goal attainment in adolescents and young adults (AYA) with type 1 diabetes (T1D) on a daily basis.
Methods
...Eight‐eight AYA (mean 17.6 ± 2.6 years, 54% female, HbA1c 7.9 ± 1.4%, diabetes duration 8.5 ± 4.5 years) with T1D who use Continuous Glucose Monitoring (CGM) completed a 2‐week prospective study. Participants chose a self‐management goal to focus on during participation. For six days, participants prospectively completed a 25‐item Engagement Prediction Survey to assess biopsychosocial factors to predict daily diabetes outcomes and an end‐of‐day Goal Survey. Lasso and mixed‐model regression were used to determine items in the Engagement Prediction Survey most predictive of perceived goal attainment, CGM Time‐in‐Range (TIR, 70–180 mg/dl), sensor mean glucose, number of insulin boluses and hyperglycemia response (bolus within 30 min of high alert or glucose <200 mg/dl within 2 hours).
Results
A 7‐item model (including current glucose, planning/wanting to manage diabetes, wanting to skip self‐management, feeling good about self, health perception and support needs) explained 16.7% of the daily variance in TIR, 18.6% of mean sensor glucose, 2.1% of the number of boluses, 14% of hyperglycemia response, and 28.7% of goal attainment perceptions. The mean absolute change in day‐to‐day TIR was 16%, sensor glucose was 30 mg/dl, and the number of boluses was 2. AYA reported more positive Engagement Prediction Survey responses on mornings when they awoke with lower glucose levels.
Conclusions
Morning biopsychosocial state factors predict glycemic and adherence outcomes in AYA with diabetes and could be a novel intervention target for future behavioural interventions.
Quality sleep is important for youth with type 1 diabetes (T1D) and their parents, yet disrupted sleep due to diabetes-related awakenings is common. Increased time-in-range (TIR, 70-180 mg/dL) and ...decreased time in hypoglycemia (<70 mg/dL) may positively affect sleep. This analysis evaluated associations between glycemia and sleep measures. Youth age 3-17 years in an observational study had sleep and glycemic data collected. TIR and time in hypoglycemia throughout the day were compared with actigraphy data, including total sleep time (TST), sleep efficiency, wake after sleep onset (WASO), and number of awakenings. Linear mixed models were used to test associations between sleep variables with TIR and hypoglycemia. Twenty-six youth with T1D (mean age 10.7±4.0 yrs, median T1D duration 2.0 yrs IQR 0.6, 4.8, HbA1c 7.2±1.4%, 50% female) and a parent were included. Significant associations were found between hypoglycemia and child WASO, awakenings, and TST. No associations were observed between TIR and sleep measures (Table 1). Time in hypoglycemia was associated with more nocturnal awakenings and WASO in youth with T1D. The association between hypoglycemia and child TST is unexpected and requires further evaluation. A larger sample size is needed to further evaluate correlations between glycemic outcomes and sleep measures. Tools to decrease hypoglycemia are important to improve sleep quality for youth with T1D.
Objective To test the hypothesis that a change in glycated hemoglobin (A1c) over a follow-up interval of approximately 2 years would be associated with concomitant changes in fasting lipids in ...individuals with type 1 diabetes (T1D). Study design All subjects with T1D diagnosed in 2002-2005 in the SEARCH for Diabetes in Youth study with at least 2 study visits ∼12 and ∼24 months after an initial visit were included (age at initial visit, 10.6 ± 4.1 years; 48% female; diabetes duration, 10 ± 7 months; 76% non-Hispanic white; A1c = 7.7% ± 1.4%). Longitudinal mixed models were fit to examine the relationship between change in A1c and change in lipid levels (total cholesterol TC, high-density lipoprotein-cholesterol HDL-c, low-density lipoprotein-cholesterol LDL-c, log triglycerides TG, and non–HDL-c) with adjustment for possible confounders. Results Change in A1c over time was significantly associated with changes in TC, HDL-c, LDL-c, TG, and non–HDL-c over the range of A1c values. For example, for a person with an A1c of 10% and then a 2% decrease in A1c 2 years later (to 8%), the model predicted concomitant changes in TC (−0.29 mmol/L, −11.4 mg/dL), HDL-c (0.03 mmol/L, 1.3 mg/dL), LDL-c (−0.23 mmol/L, −9.0 mg/dL), and non–HDL-c (−0.32 mmol/L, −12.4 mg/dL) and an 8.5% decrease in TG (mmol/L). Conclusions Improved glucose control over a 2-year follow-up was associated with a more favorable lipid profile but may be insufficient to normalize lipids in dyslipidemic T1D youth needing to decrease lipids to goal.
Glycemic control is particularly challenging for toddlers and preschoolers with type 1 diabetes (T1D), and data on the use of closed-loop systems in this age range are limited.
We studied use of a ...modified investigational version of the Tandem t:slim X2 Control-IQ system in children aged 2 to 5 years during 48 h in an outpatient supervised hotel (SH) setting followed by 3 days of home use to examine the safety of this system in young children. Meals and snacks were not restricted and boluses were estimated per parents' usual routine. At least 30 min of daily exercise was required during the SH phase. All participants were remotely monitored by study staff while on closed-loop in addition to monitoring by at least one parent throughout the study.
Twelve participants diagnosed with T1D for at least 3 months with mean age 4.7 ± 1.0 years (range 2.0-5.8 years) and hemoglobin A1c of 7.3% ± 0.8% were enrolled at three sites. With use of Control-IQ, the percentage of participants meeting our prespecified goals of less than 6% time below 70 mg/dL and less than 40% time above 180 mg/dL increased from 33% to 83%. Control-IQ use significantly improved percent time in range (70-180 mg/dL) compared to baseline (71.3 ± 12.5 vs. 63.7 ± 15.1,
= 0.016). All participants completed the study with no adverse events.
In this brief pilot study, use of the modified Control-IQ system was safe in 2-5-year-old children with T1D and improved glycemic control.
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