The effect of additional mealtime insulin bolus using an insulin-to-protein ratio compared to usual carbohydrate counting on postprandial glucose in those with type 1 diabetes who usually follow a carbohydrate-restricted diet: A randomized cross-over trial

A randomised controlled trial of additional bolus insulin using an insulin-to-protein ratio compared with insulin-to-carbohdrate ratio alone in people with type 1 diabetes following a carbohydrate-restricted diet

AIMS

Postprandial hyperglycemia can be problematic for people with type 1 diabetes (T1DM) following carbohydrate-restricted diets. Bolus insulin calculated for meal protein plus carbohydrate may help. This study evaluated the effect of additional bolus insulin using an insulin-to-protein ratio (IPR) on glycaemic control.

MATERIALS AND METHODS

Participants with T1DM aged ≥18-years were randomly allocated (1:1) to either carbohydrate and protein-based, or carbohydrate-based insulin dosing alone for 12 weeks while following a carbohydrate-restricted diet (50-100 g/day). Measurement of HbA1c and continuous glucose monitoring occurred at baseline and 12 weeks, with assessment of participant experience at 12 weeks.

RESULTS

Thirty-four participants were randomised, 22 female, mean(SD): age 39.2 years (12.6) years; diabetes duration 20.6 years (12.9); HbA1c 7.3 % (0.8), 56.7 mmol/mol (9.2). Seven in each group used insulin pump therapy. HbA1c reduced at 12 weeks with no difference between treatments: mean (SD) control 7.2 % (1.0), 55.7 mmol/mol (10.6); intervention 6.9 % (0.7), 52.3 mmol/mol (7.2) (p = 0.65). Using additional protein-based insulin dosing compared with carbohydrate alone, there was no difference in glycaemic variability, time spent in euglycemic range (TIR), or below range. Participants using IPR reported more control of their diabetes, but varying levels of distress.

CONCLUSIONS

Additional bolus insulin using an IPR did not improve glycaemic control or TIR in patients with well controlled T1DM following a carbohydrate-restricted diet. Importantly, the use of the IPR does not increase the risk of hypoglycemia and may be preferred.

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).

The effect of the modified fat-protein unit algorithm compared with that of carbohydrate counting on postprandial glucose in adults with type-1 diabetes when consuming meals with differing macronutrient compositions: a randomized crossover trial

BACKGROUND

The optimization of glucose control in type-1 diabetes is challenged by postprandial glycemic variability. This study aimed to compare the postprandial glycemic effects of carbohydrate counting and the modified fat-protein unit (FPU) algorithms following meals with different protein and fat emphases in adults with type-1 diabetes.

METHODS

Thirty adults with type-1 diabetes aged 18 to 45 years participated in a randomized crossover trial. In a random order, participants consumed four test meals with equivalent energy and different macronutrient emphases on four separate mornings. The modified FPU algorithms and carbohydrate counting were used to determine the insulin dose for the test meals. A continuous glucose monitoring system was used to measured postprandial glycemia.

RESULTS

Compared with carbohydrate counting, the modified FPU algorithm significantly decreased the late postprandial mean glucose levels (p = 0.026) in high protein-fat meals. The number of hypoglycemia episodes was similar between insulin dosing algorithms for the high protein-fat meals; hypoglycemic events were considerably higher for the modified FPU in the normal protein-fat meal (p = 0.042).

CONCLUSIONS

The modified FPU algorithm may improve postprandial glycemic control after consuming high protein-fat meals in adults with type-1 diabetes but may result in increased hypoglycemia risk when used with a normal protein-fat meal.

Effects of a low-carbohydrate diet in adults with type 1 diabetes management: A single arm non-randomised clinical trial

Public interest in low-carbohydrate (LC) diets for type 1 diabetes (T1D) management has increased. This study compared the effects of a healthcare professional delivered LC diet compared to habitual diets higher in carbohydrates on clinical outcomes in adults with T1D. Twenty adults (18-70 yrs) with T1D (≥6 months duration) with suboptimal glycaemic control (HbA1c>7.0% or >53 mmol/mol) participated in a 16-week single arm within-participant, controlled intervention study involving a 4-week control period following their habitual diets (>150 g/day of carbohydrates) and a 12-week intervention period following a LC diet (25-75 g/day of carbohydrates) delivered remotely by a registered dietitian. Glycated haemoglobin (HbA1c -primary outcome), time in range (blood glucose: 3.5-10.0 mmol/L), frequency of hypoglycaemia (<3.5 mmol/L), total daily insulin, and quality of life were assessed before and after the control and intervention periods. Sixteen participants completed the study. During the intervention period, there were reductions in total dietary carbohydrate intake (214 to 63 g/day; P<0.001), HbA1c (7.7 to 7.1% or 61 to 54 mmol/mol; P = 0.003) and total daily insulin use (65 to 49 U/day; P<0.001), increased time spent in range (59 to 74%; P<0.001), and improved quality of life (P = 0.015), with no significant changes observed during the control period. Frequency of hypoglycaemia episodes did not differ across timepoints, and no episodes of ketoacidosis or other adverse events were reported during the intervention period. These preliminary findings suggest that a professionally supported LC diet may lead to improvements in markers of blood glucose control and quality of life with reduced exogenous insulin requirements and no evidence of increased hypoglycaemia or ketoacidosis risk in adults with T1D. Given the potential benefits of this intervention, larger, longer-term randomised controlled trials are warranted to confirm these findings. Trial Registration: https://www.anzctr.org.au/ACTRN12621000764831.aspx.

Insulin-to-carbohydrate ratios: An overview for nurses

ABSTRACT

Carbohydrate counting is an efficacious technique to safely achieve glycemic targets and improve outcomes for patients using meal-time insulin. This article provides nurses with the knowledge and skills to assist and support their patients. It also reviews how to evaluate glycemic control and provides guidelines for referral to diabetes education programs.

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

High-protein meals require 30% additional insulin to prevent delayed postprandial hyperglycaemia

AIM

To determine the amount of additional insulin required for a high-protein meal to prevent postprandial hyperglycaemia in individuals with type 1 diabetes using insulin pump therapy.

METHODS

In this randomized cross-over study, 26 participants aged 8-40 years, HbA_1c < 65 mmol/mol (8.1%), received a 50 g protein, 30 g carbohydrate, low-fat (< 1 g) breakfast drink over five consecutive days at home. A standard insulin dose (100%) was compared with additional doses of 115, 130, 145 and 160% for the protein, in randomized order. Doses were commenced 15-min pre-drink and delivered over 3 h using a combination bolus with 65% of the standard dose given up front. Postprandial glycaemia was assessed by 4 h of continuous glucose monitoring.

RESULTS

The 100% dosing resulted in postprandial hyperglycaemia. From 120 min, ≥ 130% doses resulted in significantly lower postprandial glycaemic excursions compared with 100% (P < 0.05). A 130% dose produced a mean (sd) glycaemic excursion that was 4.69 (2.42) mmol/l lower than control, returning to baseline by 4 h (P < 0.001). From 120 min, there was a significant increase in the risk of hypoglycaemia compared with control for 145% [odds ratio (OR) 25.4, 95% confidence interval (CI) 5.5-206; P < 0.001) and 160% (OR 103, 95% CI 19.2-993; P < 0.001). Some 81% (n = 21) of participants experienced hypoglycaemia following a 160% dose, whereas 58% (n = 15) experienced hypoglycaemia following a 145% dose. There were no hypoglycaemic events reported with 130%.

CONCLUSIONS

The addition of 30% more insulin to a standard dose for a high-protein meal, delivered using a combination bolus, improves postprandial glycaemia without increasing the risk of hypoglycaemia.

Carbohydrate Intake in the Context of Exercise in People with Type 1 Diabetes

Although the benefits of regular exercise on cardiovascular risk factors are well established for people with type 1 diabetes (T1D), glycemic control remains a challenge during exercise. Carbohydrate consumption to fuel the exercise bout and/or for hypoglycemia prevention is an important cornerstone to maintain performance and avoid hypoglycemia. The main strategies pertinent to carbohydrate supplementation in the context of exercise cover three aspects: the amount of carbohydrates ingested (i.e., quantity in relation to demands to fuel exercise and avoid hypoglycemia), the timing of the intake (before, during and after the exercise, as well as circadian factors), and the quality of the carbohydrates (encompassing differing carbohydrate types, as well as the context within a meal and the associated macronutrients). The aim of this review is to comprehensively summarize the literature on carbohydrate intake in the context of exercise in people with T1D.

Impact of dietary protein on postprandial glycaemic control and insulin requirements in Type 1 diabetes: a systematic review

AIM

Postprandial hyperglycaemia is a challenge for people living with Type 1 diabetes. In addition to carbohydrate, dietary protein has been shown to contribute to postprandial glycaemic excursions with recommendations to consider protein when calculating mealtime insulin doses. The aim of this review is to identify and synthesize evidence about the glycaemic impact of dietary protein and insulin requirements for individuals with Type 1 diabetes.

METHODS

A systematic literature search of relevant biomedical databases was performed to identify research on the glycaemic impact of dietary protein when consumed alone, and in combination with other macronutrients in individuals with Type 1 diabetes.

RESULTS

The review included 14 published studies dated from 1992 to 2018, and included studies that researched the impact of protein alone (n = 2) and protein in a mixed meal (n = 12). When protein was consumed alone a glycaemic effect was not seen until ≥ 75 g. In a carbohydrate-containing meal ≥ 12.5 g of protein impacted the postprandial glucose. Inclusion of fat in a high-protein meal enhanced the glycaemic response and further increased insulin requirements. The timing of the glycaemic effect from dietary protein ranged from 90 to 240 min. Studies indicate that the postprandial glycaemic response and insulin requirements for protein are different when protein is consumed alone or with carbohydrate and/or fat.

CONCLUSIONS

This systematic review provides evidence that dietary protein contributes to postprandial glycaemic excursions and insulin requirements. These insights have important implications for the education of people with Type 1 diabetes and highlights the need for more effective insulin dosing strategies for mixed macronutrient meals.