Impact of macronutrient content of meals on postprandial glucose control in the context of closed-loop insulin delivery: A randomized cross-over study

Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

A model of subcutaneous pramlintide pharmacokinetics and its effect on gastric emptying: Proof-of-concept based on populational data

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Background

Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).

Objective

In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?

Methods

We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.

Results

Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.

Conclusion

Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

A Nutritional Approach to Optimizing Pump Therapy in Type 1 Diabetes Mellitus

Achieving optimal glucose control in individuals with type 1 diabetes (T1DM) continues to pose a significant challenge. While continuous insulin infusion systems have shown promise as an alternative to conventional insulin therapy, there remains a crucial need for greater awareness regarding the necessary adaptations for various special circumstances. Nutritional choices play an essential role in the efficacy of diabetes management and overall health status for patients with T1DM. Factors such as effective carbohydrate counting, assessment of the macronutrient composition of meals, and comprehending the concept of the glycemic index of foods are paramount in making informed pre-meal adjustments when utilizing insulin pumps. Furthermore, the ability to handle such situations as physical exercise, illness, pregnancy, and lactation by making appropriate adjustments in nutrition and pump settings should be cultivated within the patient-practitioner relationship. This review aims to provide healthcare practitioners with practical guidance on optimizing care for individuals living with T1DM. It includes recommendations on carbohydrate counting, managing mixed meals and the glycemic index, addressing exercise-related challenges, coping with illness, and managing nutritional needs during pregnancy and lactation. Additionally, considerations relating to closed-loop systems with regard to nutrition are addressed. By implementing these strategies, healthcare providers can better equip themselves to support individuals with T1DM in achieving improved diabetes management and enhanced quality of life.

Post-Exercise Protein Intake May Reduce Time in Hypoglycemia Following Moderate-Intensity Continuous Exercise among Adults with Type 1 Diabetes

Little is known about the role of post-exercise protein intake on post-exercise glycemia. Secondary analyses were conducted to evaluate the role of post-exercise protein intake on post-exercise glycemia using data from an exercise pilot study. Adults with T1D (n = 11), with an average age of 33.0 ± 11.4 years and BMI of 25.1 ± 3.4, participated in isoenergetic sessions of high-intensity interval training (HIIT) or moderate-intensity continuous training (MICT). Participants completed food records on the days of exercise and provided continuous glucose monitoring data throughout the study, from which time in range (TIR, 70-180 mg/dL), time above range (TAR, >180 mg/dL), and time below range (TBR, <70 mg/dL) were calculated from exercise cessation until the following morning. Mixed effects regression models, adjusted for carbohydrate intake, diabetes duration, and lean mass, assessed the relationship between post-exercise protein intake on TIR, TAR, and TBR following exercise. No association was observed between protein intake and TIR, TAR, or TBR (p-values ≥ 0.07); however, a borderline significant reduction of -1.9% (95% CI: -3.9%, 0.0%; p = 0.05) TBR per 20 g protein was observed following MICT in analyses stratified by exercise mode. Increasing post-exercise protein intake may be a promising strategy to mitigate the risk of hypoglycemia following MICT.

Effect of Isocaloric Meals on Postprandial Glycemic and Metabolic Markers in Type 1 Diabetes-A Randomized Crossover Trial

The aim of this study was to assess the effect of four isocaloric meals with different macronutrient compositions on postprandial blood glucose, lipids, and glucagon in adults with type 1 diabetes (T1D). Seventeen subjects tested four isocaloric meals in a randomized crossover design. The meal compositions were as follows: high-carbohydrate (HC); high-carbohydrate with extra fiber (HC-fiber); low-carbohydrate high-protein (HP); and low-carbohydrate high-fat (HF). Blood glucose and lipid measurements were collected up to 4 h and glucagon up to 3 h postprandially. Mean postprandial glucose excursions were lower after the HP compared to the HC (p = 0.036) and HC-fiber meals (p = 0.002). There were no differences in mean glucose excursions after the HF meal compared to the HC and HP meals. The HF meal resulted in higher triglyceride excursions compared to the HP meal (p < 0.001) but not compared to the HC or HC-fiber meals. Glucagon excursions were higher at 180 min after the HP meal compared to the HC and HF meals. In conclusion, the low-carbohydrate HP meal showed the most favorable glycemic and metabolic effects during a 4 h postprandial period in subjects with T1D.

A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes

OBJECTIVE

Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the noninferiority of qualitative meal-size estimation strategy.

RESEARCH DESIGN AND METHODS

We conducted a two-center, randomized, crossover, noninferiority trial to compare 3 weeks of automated insulin delivery with 1) CHO counting and 2) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30-60 g, 60-90 g, and >90 g CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin to CHO ratios multiplied by 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9-10.0 mmol/L, with a predefined noninferiority margin of 4%.

RESULTS

A total of 30 participants completed the study (n = 20 women; age 44 (SD 17) years; A1C 7.4% [0.7%]). The mean time in the 3.9-10.0 mmol/L range was 74.1% (10.0%) with CHO counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference was -3.6% (8.3%; noninferiority P = 0.78). Frequencies of times at <3.9 mmol/L and <3.0 mmol/L were low (<1.6% and <0.2%) in both arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 units/day; P = 0.003).

CONCLUSIONS

Though the qualitative meal-size estimation method achieved a high time in range and low time in hypoglycemia, noninferiority was not confirmed.

Insulin delivery patterns required to maintain postprandial euglycemia in type 1 diabetes following consumption of traditional Egyptian Ramadan Iftar meal using insulin pump therapy: A randomized crossover trial

OBJECTIVES

During Ramadan, traditional Egyptian Iftar meals have large amounts of high-glycemic index carbohydrate and fat. The efficacy of different bolus regimens on optimizing post prandial glucose (PPG) excursion following this Iftar meal was assessed.

METHODS

A randomized controlled trial evaluating 4-h PPG measured by continuous glucose-monitoring was conducted. A total of 25 youth with T1DM using insulin pumps were given the same Iftar meal (fat [45 g], protein [28 g], CHO [95 g]) on seven consecutive days. Insulin to carbohydrate ratio (ICR) was individualized, and all boluses were given upfront 20 min before Iftar. Participants were randomized to receive a standard bolus and six different split boluses delivered over 4 h in the following splits: dual wave (DW) 50/50; DW 50/50 with 20% increment (120% ICR); DW60/40; DW 60/40 with 20% increment; DW 70/30 and DW 70/30 with 20% increment.

RESULTS

Standard bolus and split 70/30 with 20% increment resulted in significantly lower early glucose excursions (120 min) with mean excursions of less than 40 mg/dL (2.2 mmol/L) compared to other conditions (p < 0.01). The split 70/30 with 20% increment significantly optimized late PPG excursion (240 min) in comparison to standard bolus (p < 0.01), as well as resulting in a significantly lower post meal glucose area under the curve compared with all other conditions (p < 0.01), with no late hypoglycemia.

CONCLUSION

To achieve physiologic PPG profile in traditional Iftar meal, a DW bolus with 20% increment given 20 min preprandial as split bolus 70/30 over 4 h, optimized both early and delayed PPG excursions.

Efficacy of insulin dosing algorithms for high-fat high-protein mixed meals to control postprandial glycemic excursions in people living with type 1 diabetes: A systematic review and meta-analysis

Optimizing postprandial blood glucose (PPG) levels after mixed meals that contain high fat and protein remains a challenge in the treatment of type 1 diabetes. This study evaluated the efficacy of different algorithms used for dosing insulin based on counting units of high fat and high protein (HFHP) meals with the current conventional method of counting carbohydrates alone to control PPG excursions. The MEDLINE, EMBASE, and Cochrane electronic databases were searched, with the analysis restricted to randomized control trials (RCTs). The primary outcome was the PPG (mean and standard deviation) at 240 min. The pooled final estimate was the mean difference (MD) of the PPGs at 240 min using random effect models to account for heterogeneity. In total, 15 studies were identified and included in the systemic review, of which 12 were RCTs, and three studies were non-randomized trials. The pooled MD of the PPG at 240 min was in favor of additional insulin doses in HFHP meals compared to the carbohydrate counting alone. The statistically significant results favored the combined bolus (30:70) that split over 2 h in insulin pump therapy with pooled MD of the PPG, 240 min of -24.65; 95% CI: -36.59, -8.41; and heterogeneity, 0%. Other statistically significant results favored the additional insulin added to insulin to carb ratio (ICR) of meal bolus (25-60% ICR) in multiple daily injections therapy with the pooled MD of PPG at 240 min, -21.71; 95% CI: -38.45, -4.73; and heterogeneity, 18%. Insulin treatment based on fat and protein content, in addition to carbohydrate counting, is more effective than the carbohydrate counting method alone; however, further research is warranted to determine the best equation for fat and protein counting, particularly in people with multiple daily injections.

Insulin strategies for dietary fat and protein in type 1 diabetes: A systematic review

AIM

To identify and report the efficacy of insulin strategies used to manage glycaemia following fat and/or fat and protein meals in type 1 diabetes.

METHODS

A systematic literature search of medical databases from 1995 to 2021 was undertaken. Inclusion criteria were randomised controlled trials that reported at least one of the following glycaemic outcomes: mean glucose, area under the curve, time in range or hypoglycaemic episodes.

RESULTS

Eighteen studies were included. Thirteen studies gave additional insulin. Five studies gave an additional 30%-43% of the insulin-to-carbohydrate ratio (ICR) for 32-50 g of fat and 31%-51% ICR for 7-35 g of fat with 12-27 g of protein added to control meals. A further eight studies gave -28% to +75% ICR using algorithms based on fat and protein for meals with 19-50 g of carbohydrate, 2-79 g of fat and 10-60 g of protein, only one study reported a glycaemic benefit of giving less than an additional 24% ICR. Eight studies evaluated insulin delivery patterns. Four of six studies in pump therapy, and one of two studies in multiple daily injections showed the combination of bolus and split dose, respectively, were superior. Five studies examined the insulin dose split, four demonstrated 60%-125% ICR upfront was necessary. Two studies investigated the timing of insulin delivery, both reported administration 15 min before the meal lowered postprandial glycaemia.

CONCLUSIONS

Findings highlight the glycaemic benefit of an additional 24%-75% ICR for fat and fat and protein meals. For these meals, there is supportive evidence for insulin delivery in a combination bolus with a minimum upfront dose of 60% ICR, 15 min before the meal.

In children and young people with type 1 diabetes using Pump therapy, an additional 40% of the insulin dose for a high-fat, high-protein breakfast improves postprandial glycaemic excursions: A cross-over trial

AIM

To determine the insulin requirement for a high-fat, high-protein breakfast to optimise postprandial glycaemic excursions in children and young people with type 1 diabetes using insulin pumps.

METHODS

In all, 27 participants aged 10-23 years, BMI <95^th percentile (2-18 years) or BMI <30 kg/m² (19-25 years) and HbA_1c ≤64 mmol/mol (≤8.0%) consumed a high-fat, high-protein breakfast (carbohydrate: 30 g, fat: 40 g and protein: 50 g) for 4 days. In this cross-over trial, insulin was administered, based on the insulin-to-carbohydrate ratio (ICR) of 100% (control), 120%, 140% and 160%, in an order defined by a randomisation sequence and delivered in a combination bolus, 60% ¼ hr pre-meal and 40% over 3 hr. Postprandial sensor glucose was assessed for 6 hr.

RESULTS

Comparing 100% ICR, 140% ICR and 160% ICR resulted in significantly lower 6-hr areas under the glucose curves: mean (95%CI) (822 mmol/L.min [605,1039] and 567 [350,784] vs 1249 [1042,1457], p ≤ 0.001) and peak glucose excursions (4.0 mmol/L [3.0,4.9] and 2.7 [1.7,3.6] vs 6.0 [5.0,6.9],p < 0.001). Rates of hypoglycaemia for 100%-160% ICR were 7.7%, 7.7%, 12% and 19% respectively (p ≥ 0.139). With increasing insulin dose, a step-wise reduction in mean glucose excursion was observed from 1 to 6 hr (p = 0.008).

CONCLUSIONS

Incrementally increasing the insulin dose for a high-fat, high-protein breakfast resulted in a predictable, dose-dependent reduction in postprandial glycaemia: 140% ICR improved postprandial glycaemic excursions without a statistically significant increase in hypoglycaemia. These findings support a safe, practical method for insulin adjustment for high-fat, high-protein meals that can be readily implemented in practice to improve postprandial glycaemia.

For a high fat, high protein breakfast, preprandial administration of 125% of the insulin dose improves postprandial glycaemic excursions in people with type 1 diabetes using multiple daily injections: A cross-over trial

AIM

To determine the glycaemic impact of an increased insulin dose, split insulin dose and regular insulin for a high fat, high protein breakfast in people with type 1 diabetes using multiple daily injections (≥4/day).

METHODS

In this cross-over trial, participants received the same high fat, high protein breakfast (carbohydrate:30 g, fat:40 g, protein:50 g) for 4 days. Four different insulin strategies were randomly allocated and tested; 100% of the insulin-to-carbohydrate ratio (ICR) given in a single dose using aspart insulin (100Asp), 125% ICR given in a single dose using aspart (125Asp) or regular insulin (125Reg) and 125% ICR given in a split dose using aspart insulin (100:25Asp). Insulin was given 0.25 hr pre-meal and for 100:25Asp, also 1 hr post-meal. Postprandial sensor glucose was measured for 5 hr.

RESULTS

In all, 24 children and adults were participated. The 5-hr incremental area under the curves for 100Asp, 125Asp, 125Reg and 100:25Asp were 620 mmol/L.min [95% CI: 451,788], 341 mmol/L.min [169,512], 675 mmol/L.min [504,847] and 434 mmol/L.min [259,608], respectively. The 5-hr incremental area under the curve for 125Asp was significantly lower than for 100Asp (p = 0.016) and for 125Reg (p = 0.002). There was one episode of hypoglycaemia in 125Reg.

CONCLUSIONS

For a high fat, high protein breakfast, giving 125% ICR preprandially, using aspart insulin significantly improved postprandial glycaemia without hypoglycaemia. There was no additional glycaemic benefit from giving insulin in a split dose (100:25%) or replacing aspart with regular insulin.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.

Effect of nutrition on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully automated closed-loop insulin therapy

Fully automated closed-loop insulin delivery may offer a novel way to manage diabetes in hospital. However, postprandial glycaemic control remains challenging. We aimed to assess the effect of nutritional intake on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully closed-loop insulin therapy. The effects of different meal types and macronutrient composition on sensor glucose time-in-target (TIT, 3.9-10.0 mmol/L) and mean sensor glucose were assessed with hierarchical linear models using a Bayesian estimation approach. TIT was lower and the mean sensor glucose slightly higher, after breakfast compared with lunch and dinner, whereas the insulin dose was higher. Across meals, when carbohydrates were replaced by fat, or to a lesser extent by protein, postprandial glucose control improved. For breakfast, a 3.9% improvement in TIT was observed when 10% of the energy from carbohydrates was replaced by fat. Improvements were slightly lower during lunch and dinner (3.2% and 3.4%) or when carbohydrates were replaced by protein (2.2 and 2.7%, respectively). We suggest that reducing carbohydrate at the expense of fat or protein, could further improve glucose control during fully closed-loop insulin therapy in hospital.

Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL × min (95% confidence interval [CI] 3190.5-18,648.0, P = 0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6-77.3, P = 0.03) for peak continuous glucose monitor glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08-0.63, P = 0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0-2 h insulin delivery decreased by 0.45 U for pasta (P = 0.001) and by 0.27 U for white rice (P = 0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting. Clinical Trial Registry: clinicaltrials.gov NCT03767790.

A single-blind, randomised, crossover study to reduce hypoglycaemia risk during postprandial exercise with closed-loop insulin delivery in adults with type 1 diabetes: announced (with or without bolus reduction) vs unannounced exercise strategies

AIMS/HYPOTHESIS

For individuals living with type 1 diabetes, closed-loop insulin delivery improves glycaemic control. Nonetheless, maintenance of glycaemic control during exercise while a prandial insulin bolus remains active is a challenge even to closed-loop systems. We investigated the effect of exercise announcement on the efficacy of a closed-loop system, to reduce hypoglycaemia during postprandial exercise.

METHODS

A single-blind randomised, crossover open-label trial was carried out to compare three strategies applied to a closed-loop system at mealtime in preparation for exercise taken 90 min after eating at a research testing centre: (1) announced exercise to the closed-loop system (increases target glucose levels) in addition to a 33% reduction in meal bolus (A-RB); (2) announced exercise to the closed-loop system and a full meal bolus (A-FB); (3) unannounced exercise and a full meal bolus (U-FB). Participants performed 60 min of exercise at 60% [Formula: see text] 90 min after eating breakfast. The investigators were not blinded to the interventions. However, the participants were blinded to the sensor glucose readings and to the insulin infusion rates throughout the intervention visits.

RESULTS

The trial was completed by 37 adults with type 1 diabetes, all using insulin pumps: mean±SD, 40.0 ± 15.0 years of age, HbA_1c 57.1 ± 10.8 mmol/mol (7.3 ± 1.0%). Reported results were based on plasma glucose values. During exercise and the following 1 h recovery period, time spent in hypoglycaemia (<3.9 mmol/l; primary outcome) was reduced with A-RB (mean ± SD; 2.0 ± 6.2%) and A-FB (7.0 ± 12.6%) vs U-FB (13.0 ± 19.0%; p < 0.0001 and p = 0.005, respectively). During exercise, A-RB had the least drop in plasma glucose levels: A-RB -0.3 ± 2.8 mmol/l, A-FB -2.6 ± 2.9 mmol/l vs U-FB -2.4 ± 2.7 mmol/l (p < 0.0001 and p = 0.5, respectively). Comparison of A-RB vs U-FB revealed a decrease in the time spent in target (3.9-10 mmol/l) by 12.7% (p = 0.05) and an increase in the time spent in hyperglycaemia (>10 mmol/l) by 21% (p = 0.001). No side effects were reported during the applied strategies.

CONCLUSIONS/INTERPRETATION

Combining postprandial exercise announcement, which increases closed-loop system glucose target levels, with a 33% meal bolus reduction significantly reduced time spent in hypoglycaemia compared with the other two strategies, yet at the expense of more time spent in hyperglycaemia.

TRIAL REGISTRATION

ClinicalTrials.gov NCT0285530 FUNDING: JDRF (2-SRA-2016-210-A-N), the Canadian Institutes of Health Research (354024) and the Fondation J.-A. DeSève chair held by RR-L.

Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

Single-Hormone Artificial Pancreas Use in Diabetes: Clinical Efficacy and Remaining Challenges

IN BRIEF Artificial pancreas systems are rapidly developing and constitute the most promising technology for insulin-requiring diabetes management. Single-hormone systems (SH-AP) that deliver only insulin and have a hybrid design that necessitates patients' interventions around meals and exercise are the first to appear on the market. Trials with SH-AP have demonstrated improvement in time spent with blood glucose levels within target ranges, with a concomitant decrease in hypoglycemia. Longer and larger trials involving different patient populations are ongoing to further advance this important technology.

Postprandial Glucose Control in Type 1 Diabetes: Importance of the Gastric Emptying Rate

The achievement of optimal post-prandial (PP) glucose control in patients with type 1 diabetes (T1DM) remains a great challenge. This review summarizes the main factors contributing to PP glucose response and discusses the likely reasons why PP glucose control is rarely achieved in T1DM patients. The macronutrient composition of the meal, the rate of gastric emptying and premeal insulin administration are key factors affecting the PP glucose response in T1DM. Although the use of continuous insulin infusion systems has improved PP glucose control compared to conventional insulin therapy, there is still need for further ameliorations. T1DM patients frequently present a delayed gastric emptying (GE) that produces a lower but more prolonged PP hyperglycemia. In addition, delayed GE is associated with a longer time to reach the glycemic peak, with a consequent mismatch between PP glucose elevation and the timing of premeal insulin action. On this basis, including GE time and meal composition in the algorithms for insulin bolus calculation of the insulin delivery systems could be an important step forward for optimization of PP glucose control in T1DM.