Paediatric estimated average glucose in children with Type 1 diabetes

An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes

BACKGROUND

Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D).

METHODS

We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample's CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period.

RESULTS

The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D.

CONCLUSIONS

Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.

Usefulness of estimated average glucose (eAG) in glycemic control and cardiovascular risk reduction

OBJECTIVE

One of the 8 regional health authority (RHA) zones in New Brunswick, Canada has implemented eAG since 2010. We sought to evaluate the clinical outcomes of glycemic control and cardiovascular risk levels before and after the eAG implementation in this zone; and to compare the overall outcomes of this zone with other 7 zones of the province.

METHODS

Data (838,407 HbA1c values and 612,314 LDL-c values) was extracted from all adult diabetic patients in the provincial Diabetes Registry from 2008 to 2014. The Kruskal-Wallis statistic was conducted to compare the medians and inter quartile ranges of HbA1c and LDL-c from different zones. The proportion of patients achieving therapeutic targets, the distribution of HbA1c and LDL-c values pre/post the eAG implementation in RHA Zone 1.1 were assessed by Chi-square analysis.

RESULTS

The proportion of patients achieving targets in Zone 1.1 were at an intermediate level among all 8 zones and the trends of Zone 1.1 were no different than other zones. There were statistically significant differences for Zone 1.1 in the distribution of HbA1c (Z = -12.5190, P < 0.001) and LDL-c (Z = 16.4410, P < 0.001) before and after the eAG reported. The proportion of patients with HbA1c < 53 mmol/mol (7.0%) of the RHA Zone 1.1 was significantly lower after eAG reported (49.85% vs. 47.24%, P < 0.001); while the proportion of patients with LDL-c < 2.6 mmol/L showed statistically significant increase (68.56% vs. 71.90%, P < 0.001).

CONCLUSION

The utilization of eAG has demonstrated no significant impact on glycemic control and cardiovascular risk reduction.

Hemoglobin A1c Accurately Predicts Continuous Glucose Monitoring-Derived Average Glucose in Youth and Young Adults With Cystic Fibrosis

OBJECTIVE

In cystic fibrosis (CF), hemoglobin A_1c (HbA_1c) is thought to underestimate glycemia. However, few studies have directly assessed the relationship between HbA_1c and average glucose in CF. We determined the relationships among glycemic markers-HbA_1c, fructosamine (FA), glycated albumin (%GA), and 1,5-anhydroglucitol (1,5-AG)-and continuous glucose monitoring (CGM) in CF, hypothesizing that alternate markers would better predict average sensor glucose (ASG) than HbA_1c.

RESEARCH DESIGN AND METHODS

CF participants and a group of healthy control subjects (HCs), ages 6-25 years, wore CGM for up to 7 days. Pearson correlations assessed the relationships between CGM variables and HbA_1c, FA, %GA, and 1,5-AG. The regression line between HbA_1c and ASG was compared in CF versus HC. Linear regressions determined whether alternate markers predicted ASG after adjustment for HbA_1c.

RESULTS

CF (n = 93) and HC (n = 29) groups wore CGM for 5.2 ± 1 days. CF participants were 14 ± 3 years of age and 47% were male, with a BMI z score -0.1 ± 0.8 and no different from HCs in age, sex, or BMI. Mean HbA_1c in CF was 5.7 ± 0.8% (39 ± 9 mmol/mol) vs. HC 5.1 ± 0.2% (32 ± 2 mmol/mol) (P < 0.0001). All glycemic markers correlated with ASG (P ≤ 0.01): HbA_1c (r = 0.86), FA (r = 0.69), %GA (r = 0.83), and 1,5-AG (r = -0.26). The regression line between ASG and HbA_1c did not differ in CF versus HC (P = 0.44). After adjustment for HbA_1c, %GA continued to predict ASG (P = 0.0009) in CF.

CONCLUSIONS

HbA_1c does not underestimate ASG in CF as previously assumed. No alternate glycemic marker correlated more strongly with ASG than HbA_1c. %GA shows strong correlation with ASG and added to the prediction of ASG beyond HbA_1c. However, we are not advocating use of HbA_1c for diabetes screening in CF based on these results. Further study will determine whether glycemic measures other than ASG differ among different types of diabetes for a given HbA_1c.

Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

BACKGROUND

The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity.

METHODS

We did a random-order crossover study in volunteers at least 18 years old who had type 1 diabetes and lived within a 30 min drive of four sites in the USA. Participants were randomly assigned (1:1) in blocks of two using sequentially numbered sealed envelopes to glycaemic regulation with a bihormonal bionic pancreas or usual care (conventional or sensor-augmented insulin pump therapy) first, followed by the opposite intervention. Both study periods were 11 days in length, during which time participants continued all normal activities, including athletics and driving. The bionic pancreas was initialised with only the participant's body mass. Autonomously adaptive dosing algorithms used data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. The coprimary outcomes were the mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration less than 3·3 mmol/L, analysed over days 2-11 in participants who completed both periods of the study. This trial is registered with ClinicalTrials.gov, number NCT02092220.

FINDINGS

We randomly assigned 43 participants between May 6, 2014, and July 3, 2015, 39 of whom completed the study: 20 who were assigned to bionic pancreas first and 19 who were assigned to the comparator first. The mean CGM glucose concentration was 7·8 mmol/L (SD 0·6) in the bionic pancreas period versus 9·0 mmol/L (1·6) in the comparator period (difference 1·1 mmol/L, 95% CI 0·7-1·6; p<0·0001), and the mean time with CGM glucose concentration less than 3·3 mmol/L was 0·6% (0·6) in the bionic pancreas period versus 1·9% (1·7) in the comparator period (difference 1·3%, 95% CI 0·8-1·8; p<0·0001). The mean nausea score on the Visual Analogue Scale (score 0-10) was greater during the bionic pancreas period (0·52 [SD 0·83]) than in the comparator period (0·05 [0·17]; difference 0·47, 95% CI 0·21-0·73; p=0·0024). Body mass and laboratory parameters did not differ between periods. There were no serious or unexpected adverse events in the bionic pancreas period of the study.

INTERPRETATION

Relative to conventional and sensor-augmented insulin pump therapy, the bihormonal bionic pancreas, initialised only with participant weight, was able to achieve superior glycaemic regulation without the need for carbohydrate counting. Larger and longer studies are needed to establish the long-term benefits and risks of automated glycaemic management with a bihormonal bionic pancreas.

FUNDING

National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and National Center for Advancing Translational Sciences.

Day and night glycaemic control with a bionic pancreas versus conventional insulin pump therapy in preadolescent children with type 1 diabetes: a randomised crossover trial

BACKGROUND

The safety and efficacy of continuous, multiday, automated glycaemic management has not been tested in outpatient studies of preadolescent children with type 1 diabetes. We aimed to compare the safety and efficacy of a bihormonal bionic pancreas versus conventional insulin pump therapy in this population of patients in an outpatient setting.

METHODS

In this randomised, open-label, crossover study, we enrolled preadolescent children (aged 6-11 years) with type 1 diabetes (diagnosed for ≥1 year) who were on insulin pump therapy, from two diabetes camps in the USA. With the use of sealed envelopes, participants were randomly assigned in blocks of two to either 5 days with the bionic pancreas or conventional insulin pump therapy (control) as the first intervention, followed by a 3 day washout period and then 5 days with the other intervention. Study allocation was not masked. The autonomously adaptive algorithm of the bionic pancreas received data from a continuous glucose monitoring (CGM) device to control subcutaneous delivery of insulin and glucagon. Conventional insulin pump therapy was administered by the camp physicians and other clinical staff in accordance with their established protocols; participants also wore a CGM device during the control period. The coprimary outcomes, analysed by intention to treat, were mean CGM-measured glucose concentration and the proportion of time with a CGM-measured glucose concentration below 3·3 mmol/L, on days 2-5. This study is registered with ClinicalTrials.gov, number NCT02105324.

FINDINGS

Between July 20, and Aug 19, 2014, 19 children with a mean age of 9·8 years (SD 1·6) participated in and completed the study. The bionic pancreas period was associated with a lower mean CGM-measured glucose concentration on days 2-5 than was the control period (7·6 mmol/L [SD 0·6] vs 9·3 mmol/L [1·7]; p=0·00037) and a lower proportion of time with a CGM-measured glucose concentration below 3·3 mmol/L on days 2-5 (1·2% [SD 1·1] vs 2·8% [1·2]; p<0·0001). The median number of carbohydrate interventions given per participant for hypoglycaemia on days 1-5 (ie, glucose <3·9 mmol/L) was lower during the bionic pancreas period than during the control period (three [range 0-8] vs five [0-14]; p=0·037). No episodes of severe hypoglycaemia were recorded. Medium-to-large concentrations of ketones (range 0·6-3·6 mmol/dL) were reported on seven occasions in five participants during the control period and on no occasion during the bionic pancreas period (p=0·063).

INTERPRETATION

The improved mean glycaemia and reduced hypoglycaemia with the bionic pancreas relative to insulin pump therapy in preadolescent children with type 1 diabetes in a diabetes camp setting is a promising finding. Studies of a longer duration during which children use the bionic pancreas during their normal routines at home and school should be done to investigate the potential for use of the bionic pancreas in real-world settings.

FUNDING

The Leona M and Harry B Helmsley Charitable Trust and the US National Institute of Diabetes and Digestive and Kidney Diseases.