Behavioral Patterns and Associations with Glucose Control During 12-Week Randomized Free-Living Clinical Trial of Day and Night Hybrid Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes

Does Insulin Delivery Technology Change Our Relationship with Foods? A Scoping Review

Introduction: Automated insulin delivery (AID) systems reduce burden and improve glycemic management for people with type 1 diabetes (PwT1D) by automatically adjusting insulin as a response to measured glucose levels. There is a lack of evidence on AID and nutrition variables such as dietary intake, eating behaviors, and disordered eating. Objectives: This scoping review aims to provide a summary of the literature regarding AID and nutrition variables and to identify gaps that require further investigation. Methods: Two researchers conducted a blinded search of Medline (OVID) and PubMed for studies, including AID use (compared to non-AID use) and nutrition variables. Studies from January 2000 to July 2023 were included, as were PwT1D of all ages. Results: A total of 3132 articles were screened for appropriateness. After exclusions, 7 studies were included (2017-2023): 4 qualitative, 1 crossover, 1 randomized controlled, and 1 observational. Studies included adolescents (n = 1), adults (n = 3) or both (n = 2), and all ages (n = 1). In quantitative studies, AID was associated with lower eating distress (-0.43 ± 0.12, P = 0.004) and higher quality of life (3.1, 95% confidence interval [CI]: 0.8-5.4, P = 0.01), but not grams of carbohydrates at meals (1.0; 95% CI: -0.7 to 3.0; P = 0.24) and snacks (0.004; 95% CI: -0.8 to 0.8; P = 0.99) compared to non-AID use. In qualitative studies, AID increased the frequency and portions of food intake and led to less dietary control from parents. AID users reported eating foods higher in energy density. PwT1D were less worried about achieving accurate carbohydrate counting (CC) when using AID. Conclusions: AID use appears to influence eating behaviors, dietary patterns, and CC, although evidence was limited. AID may reduce food management burden due to the perception that AID can correct for CC inaccuracy. Significance: Further research needs to determine if AID allows for simplification of CC and improves eating behaviors while maintaining glycemic stability.

The relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio required to maintain glycaemia is non-linear in young people with type 1 diabetes: A randomized crossover trial

OBJECTIVE

To determine if the relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio (ICR) required to maintain glycaemia is linear in people with type 1 diabetes.

METHODS

We used an open labelled randomized four-arm cross-over study design. Participants (N = 31) aged 12-27 years, HbA1c ≤ 64 mmol/mol (8.0%) received insulin doses based on the individual's ICR and the study breakfast carbohydrate quantity and then consumed four breakfasts containing 20, 50, 100 and 150 g of carbohydrate over four consecutive days in randomized order. The breakfast fat and protein percentages were standardized. Postprandial glycaemia was assessed by 5 h continuous glucose monitoring. The primary outcome was percent time in range (TIR) and secondary outcomes included hypoglycaemia, glucose excursion and incremental area under the curve. Statistical analysis included linear mixed modelling and Wilcoxon signed rank tests.

RESULTS

The 20 g carbohydrate breakfast had the largest proportion of TIR (0.74 ± 0.29 p < 0.04). Hypoglycaemia was more frequent in the 50 g (n = 13, 42%) and 100 g (n = 15, 50%) breakfasts compared to the 20 g (n = 6, 20%) and 150 g (n = 7, 26%) breakfasts (p < 0.029). The 150 g breakfast glucose excursion pattern was different from the smaller breakfasts with the lowest glucose excursion 0-2 h and the highest excursion from 3.5 to 5 h.

CONCLUSIONS

A non-linear relationship between insulin requirement and breakfast carbohydrate content was observed, suggesting that strengthened ICRs are needed for meals with ≤20 and ≥150 g of carbohydrate. Meals with ≥150 g of carbohydrate may benefit from dual wave bolusing.

Dietary intake and hospitalisation due to diabetic ketoacidosis and hypoglycaemia in individuals with type 1 diabetes

We investigated the association between diet and risk of hospitalisation for diabetic ketoacidosis (DKA) or hypoglycaemia in type 1 diabetes. Food records were used to assess dietary intake. Data on DKA and hypoglycaemia hospitalisations, within two years of dietary assessments, were obtained from registries. Analyses were conducted with and without macronutrient substitution. Data were available from 1391 participants, 28 (2.0%) and 55 (4.0%) of whom were hospitalised due to DKA or hypoglycaemia, respectively. In the adjusted model, self-reported alcohol intake was associated with increased (per 10 g: B = 1.463, 95% CI = 1.114-1.922, p = 0.006; per E%: B = 1.113, 95% CI = 1.027-1.206, p = 0.009), and fibre intake with reduced (per g/MJ: B = 0.934, 95% CI = 0.878-0.995, p = 0.034) risk of DKA hospitalisation. Substituting carbohydrates for fats was associated with increased risk for hypoglycaemia hospitalisation (B = 1.361, 95% CI = 1.031-1.795, p = 0.029), while substituting alcohol for carbohydrates (B = 1.644, 95% CI = 1.006-2.685, p = 0.047) or proteins (B = 2.278, 95% CI = 1.038-4.999, p = 0.040) increased the risk for DKA hospitalisation. In conclusion, refraining from alcohol intake is a preventable risk factor for DKA, while higher fibre intake seems rather protective. Increasing carbohydrate intake while decreasing that of fats, is associated with higher hypoglycaemia risk. Whether this is a cause or effect of hypoglycaemia remains to be established.

Characterizing Glycemic Control and Sleep in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness Initiating Hybrid Closed Loop Insulin Delivery

Nocturnal hypoglycemia is life threatening for individuals with type 1 diabetes (T1D) due to loss of hypoglycemia symptom recognition (hypoglycemia unawareness) and impaired glucose counter regulation. These individuals also show disturbed sleep, which may result from glycemic dysregulation. Whether use of a hybrid closed loop (HCL) insulin delivery system with integrated continuous glucose monitoring (CGM) designed for improving glycemic control, relates to better sleep across time in this population remains unknown. The purpose of this study was to describe long-term changes in glycemic control and objective sleep after initiating hybrid closed loop (HCL) insulin delivery in adults with type 1 diabetes and hypoglycemia unawareness. To accomplish this, six adults (median age = 58 y) participated in an 18-month ongoing trial assessing HCL effectiveness. Glycemic control and sleep were measured using continuous glucose monitoring and wrist accelerometers every 3 months. Paired sample t-tests and Cohen's d effect sizes modeled glycemic and sleep changes and the magnitude of these changes from baseline to 9 months. Reduced hypoglycemia (d = 0.47-0.79), reduced basal insulin requirements (d = 0.48), and a smaller glucose coefficient of variation (d = 0.47) occurred with medium-large effect sizes from baseline to 9 months. Hypoglycemia awareness improved from baseline to 6 months with medium-large effect sizes (Clarke score (d = 0.60), lability index (d = 0.50), HYPO score (d = 1.06)). Shorter sleep onset latency (d = 1.53; p < 0.01), shorter sleep duration (d = 0.79), fewer total activity counts (d = 1.32), shorter average awakening length (d = 0.46), and delays in sleep onset (d = 1.06) and sleep midpoint (d = 0.72) occurred with medium-large effect sizes from baseline to 9 months. HCL led to clinically significant reductions in hypoglycemia and improved hypoglycemia awareness. Sleep showed a delayed onset, reduced awakening length and onset latency, and maintenance of high sleep efficiency after initiating HCL. Our findings add to the limited evidence on the relationships between diabetes therapeutic technologies and sleep health. This trial is registered with ClinicalTrials.gov (NCT03215914).

Evaluating a Glucose-Sensor-Based Tool to Help Clinicians and Adults With Type 1 Diabetes Improve Self-Management Skills

BACKGROUND

The goal of this uncontrolled pilot study was to assess the feasibility of a self-care management mobile app, called Sugar Sleuth, which incorporates the FreeStyle Libre™ glucose sensor, to help clinicians and people with type 1 diabetes (PWD) identify and mitigate self-care behaviors that contribute to glucose variability.

METHODS

PWDs with a baseline A1c between 7.5 and 9.0% used the mobile app for 14 weeks. The app prompted the PWD to enter the suspected cause of detected glycemic excursions, and to record food and insulin information. PWDs met with clinicians to collaboratively review data, identify challenges, and devise a specific self-care plan. Outcome measures included a single glycemic outcome score (SGOS) and attitude rating scales to better understand how participant attitudes could affect glycemic outcome.

RESULTS

Thirty enrolled PWDs had a mean age of 55 ± 2.6 years, and a mean diabetes duration of 32 ± 2.9 years. A significant average reduction in A1c of 0.5 ± 0.07% ( P < .01) and in mean daily carbohydrate intake of 43 ± 21 grams ( P = .05) was found. No statistically significant change in glycemic metrics, body weight, or total daily insulin dose was found. A significant negative association occurred between SGOS and "hypoglycemia tolerance" ( P = .04), and a positive correlation occurred that approached significance with "motivation to change behavior" ( P = .06).

CONCLUSIONS

These findings suggest that this mobile app system, in conjunction with CGM, provides a useful platform for helping clinicians and adults with T1D improve self-management skills to improve glycemic control.

Day-and-Night Closed-Loop Insulin Delivery in a Broad Population of Pregnant Women With Type 1 Diabetes: A Randomized Controlled Crossover Trial

OBJECTIVE

Despite advances in technology, optimal glucose control remains elusive and neonatal complications remain ubiquitous in type 1 diabetes (T1D) pregnancy. Our aim was to examine the safety, efficacy, and longer-term feasibility of day-and-night closed-loop insulin delivery.

RESEARCH DESIGN AND METHODS

We recruited 16 pregnant women (mean [SD]: age 32.8 [5.0] years, T1D duration 19.4 [10.2] years, HbA_1c 8.0% [1.1], and BMI 26.6 [4.4] kg/m²) to an open-label, randomized, crossover trial. Participants completed 28 days of closed-loop and sensor-augmented pump (SAP) insulin delivery separated by a washout period. Afterward, participants could continue to use the closed-loop system up to 6 weeks postpartum. The primary end point was the proportion of time with glucose levels within the target range (63-140 mg/dL).

RESULTS

The proportion of time with glucose levels within target was comparable during closed-loop and SAP insulin delivery (62.3 vs. 60.1% [95% CI -4.1 to 8.3]; P = 0.47). Mean glucose and time spent hyperglycemic >140 mg/dL also did not differ (131.4 vs. 131.4 mg/dL [P = 0.85] and 36.6 vs. 36.1% [P = 0.86], respectively). During closed-loop, fewer hypoglycemic episodes occurred (median 8 [range 1-17] vs. 12.5 [1-53] over 28 days; P = 0.04) and less time at <63 mg/dL (1.6 vs. 2.7%; P = 0.02). Hypoglycemia <50 mg/dL (0.24 vs. 0.47%; P = 0.03) and low blood glucose index (1.0 vs. 1.4; P = 0.01) were lower. Less nocturnal hypoglycemia (2300-0700 h) during closed-loop therapy (1.1 vs. 2.7%; P = 0.008) and a trend toward higher overnight time in target (67.7 vs. 60.6%; P = 0.06) were found.

CONCLUSIONS

Closed-loop insulin delivery was associated with comparable glucose control and significantly less hypoglycemia than SAP therapy. Larger, longer-duration multicenter trials are now indicated to determine clinical efficacy of closed-loop insulin delivery in T1D pregnancy and the impact on neonatal outcomes.

Management of Type 1 Diabetes With a Very Low-Carbohydrate Diet

OBJECTIVES

To evaluate glycemic control among children and adults with type 1 diabetes mellitus (T1DM) who consume a very low-carbohydrate diet (VLCD).

METHODS

We conducted an online survey of an international social media group for people with T1DM who follow a VLCD. Respondents included adults and parents of children with T1DM. We assessed current hemoglobin A1c (HbA1c) (primary measure), change in HbA1c after the self-reported beginning of the VLCD, total daily insulin dose, and adverse events. We obtained confirmatory data from diabetes care providers and medical records.

RESULTS

Of 316 respondents, 131 (42%) were parents of children with T1DM, and 57% were of female sex. Suggestive evidence of T1DM (based on a 3-tier scoring system in which researchers took into consideration age and weight at diagnosis, pancreatic autoimmunity, insulin requirement, and clinical presentation) was obtained for 273 (86%) respondents. The mean age at diagnosis was 16 ± 14 years, the duration of diabetes was 11 ± 13 years, and the time following a VLCD was 2.2 ± 3.9 years. Participants had a mean daily carbohydrate intake of 36 ± 15 g. Reported mean HbA1c was 5.67% ± 0.66%. Only 7 (2%) respondents reported diabetes-related hospitalizations in the past year, including 4 (1%) for ketoacidosis and 2 (1%) for hypoglycemia.

CONCLUSIONS

Exceptional glycemic control of T1DM with low rates of adverse events was reported by a community of children and adults who consume a VLCD. The generalizability of these findings requires further studies, including high-quality randomized controlled trials.

Bolusing frequency and amount impacts glucose control during hybrid closed-loop

AIM

To compare bolus insulin delivery patterns during closed-loop home studies in adults with suboptimally [HbA1c 58-86 mmol/mol (7.5%-10%)] and well-controlled [58 mmol/mol (< 7.5%)] Type 1 diabetes.

METHODS

Retrospective analysis of daytime and night-time insulin delivery during home use of closed-loop over 4 weeks. Daytime and night-time controller effort, defined as amount of insulin delivered by closed-loop relative to usual basal insulin delivery, and daytime bolus effort, defined as total bolus insulin delivery relative to total daytime insulin delivery were compared between both cohorts. Correlation analysis was performed between individual bolus behaviour (bolus effort and frequency) and daytime controller efforts, and proportion of time spent within and below sensor glucose target range.

RESULTS

Individuals with suboptimally controlled Type 1 diabetes had significantly lower bolus effort (P = 0.038) and daily bolus frequency (P < 0.001) compared with those with well-controlled diabetes. Controller effort during both daytime (P = 0.007) and night-time (P = 0.005) were significantly higher for those with suboptimally controlled Type 1 diabetes. Time when glucose was within the target range (3.9-10.0 mmol/L) during daytime correlated positively with bolus effort (r = 0.37, P = 0.016) and bolus frequency (r = 0.33, P = 0.037). Time when glucose was below the target range during daytime was comparable in both groups (P = 0.36), and did not correlate significantly with bolus effort (r = 0.28, P = 0.066) or bolus frequency (r = -0.21, P = 0.19).

CONCLUSION

More frequent bolusing and higher proportion of insulin delivered as bolus during hybrid closed-loop use correlated positively with time glucose was in target range. This emphasises the need for user input and educational support to benefit from this novel therapeutic modality.