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

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).

Postprandial glucose metabolism in children and adolescents with type 1 diabetes mellitus: potential targets for improvement

The main goal of therapeutic management of type 1 Diabetes Mellitus (T1DM) is to maintain optimal glycemic control to prevent acute and long-term diabetes complications and to enable a good quality of life. Postprandial glycemia makes a substantial contribution to overall glycemic control and variability in diabetes and, despite technological advancements in insulin treatments, optimal postprandial glycemia is difficult to achieve. Several factors influence postprandial blood glucose levels in children and adolescents with T1DM, including nutritional habits and adjustment of insulin doses according to meal composition. Additionally, hormone secretion, enteroendocrine axis dysfunction, altered gastrointestinal digestion and absorption, and physical activity play important roles. Meal-time routines, intake of appropriate ratios of macronutrients, and correct adjustment of the insulin dose for the meal composition have positive impacts on postprandial glycemic variability and long-term cardiometabolic health of the individual with T1DM. Further knowledge in the field is necessary for management of all these factors to be part of routine pediatric diabetes education and clinical practice. Thus, the aim of this report is to review the main factors that influence postprandial blood glucose levels and metabolism, focusing on macronutrients and other nutritional and lifestyle factors, to suggest potential targets for improving postprandial glycemia in the management of children and adolescents with T1DM.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

14. Children and Adolescents: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.

A new proposal for a second insulin bolus to optimize postprandial glucose profile in adolescents with type 1 diabetes

AIMS

To evaluate whether a second insulin bolus, calculated with a new approach, could improve postprandial glucose (PPG) after the intake of real-life high-fat (HF) and high-protein (HP) mixed meals.

METHODS

Fifteen adolescents with T1D treated with non-automated insulin pumps and CGM were enrolled. Patients received standard, HF and HP mixed meals treated with one pre-meal insulin bolus; based on differences in PPG between standard, HF and HP meals, correction boluses were calculated (30% and 60% of pre-meal bolus for HF and HP meals, respectively). Then patients received the same HF or HP meal treated with pre-meal bolus plus second insulin bolus after 3 h. Differences between postprandial variables after HF and HP meals treated with one or two insulin boluses were assessed by paired Student's t-test.

RESULTS

Treating HF and HP meals with two insulin boluses significantly reduced the postprandial BG-AUC (21% and 26% respectively, p < 0.05), increased %TIR (from 52.5 to 78.3% for HF meal; from 32.7 to 57.1% for HP meal; p < 0.01), and reduced mean BG and %TAR (p < 0.01), with no differences in %TBR.

CONCLUSIONS

The new way to calculate and administer correction boluses 3 h after HF and HP meals is effective and safe in reducing PPG and the hypoglycemia risk.

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.

Additional insulin dosing for fat and protein in children with type 1 diabetes using multiple daily injections

OBJECTIVE

High-fat high-protein (HFHP) meals are associated with post-prandial hyperglycemia in type 1 diabetes (T1D), administration of additional insulin for such meals is recommended in order to optimize glucose levels. Optimal timing of additional insulin for HFHP meals in children and young people receiving multiple daily injections (MDI) remains unclear.

AIM

To investigate the glycemic impact of additional insulin doses given before or after eating a HFHP meal in children with T1D using MDI.

RESEARCH DESIGN AND METHODS

A randomized, controlled three period crossover trial of 27 participants aged 13 years (6.1-17.7) at two Pediatric Diabetes centers was conducted. Additional rapid-acting insulin for the fat-protein content of a standardized HFHP meal was given at three time points + 0 + 1 + 2 h of usual pre - prandial carbohydrate insulin ; calculated using an algorithm extrapolated from current evidence base and clinical recommendations. Post-prandial glucose (PPG) parameters were calculated for 420 minutes using continuous glucose monitoring. The primary outcome was mean PPG excursion. Secondary outcomes included peak glucose, time to peak and hypoglycemia incidence.

RESULTS

There was no difference in post-prandial glucose parameters when additional HFHP insulin was administered at + 0 , + 1 , or + 2 h : mean glucose excursion (mmol/L) (SE): 1.9(0.7), 1.2(0.7), 2.5(0.7); p = 0.5); mean peak glucose (mmol/L)(SE): 10.9(0.9), 11.5(0.8), 11.5(0.9); p = 0.9; time to peak glucose (mins)(SE): 82.3(35.4), 113.6(30.9), 95.1(32.1); p = 0.8. Mild hypoglycemia was common (55%) in all groups (p = 0.97).

CONCLUSION

We found no benefit in giving additional insulin as a split dose for HFHP meals in children using MDI, mild hypoglycemia was common. Future studies would benefit from refinement of the insulin dose algorithm.

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.

The Role of Protein and Fat Intake on Insulin Therapy in Glycaemic Control of Paediatric Type 1 Diabetes: A Systematic Review and Research Gaps

Carbohydrate counting (CHC) is the established form of calculating bolus insulin for meals in children with type 1 diabetes (T1DM). With the widespread use of continuous glucose monitoring (CGM) observation time has become gapless. Recently, the impact of fat, protein and not only carbohydrates on prolonged postprandial hyperglycaemia have become more evident to patients and health-care professionals alike. However, there is no unified recommendation on how to calculate and best administer additional bolus insulin for these two macronutrients. The aim of this review is to investigate: the scientific evidence of how dietary fat and protein influence postprandial glucose levels; current recommendations on the adjustment of bolus insulin; and algorithms for insulin application in children with T1DM. A PubMed search for all articles addressing the role of fat and protein in paediatric (sub-)populations (<18 years old) and a mixed age population (paediatric and adult) with T1DM published in the last 10 years was performed. Conclusion: Only a small number of studies with a very low number of participants and high degree of heterogeneity was identified. While all studies concluded that additional bolus insulin for (high) fat and (high) protein is necessary, no consensus on when dietary fat and/or protein should be taken into calculation and no unified algorithm for insulin therapy in this context exists. A prolonged postprandial observation time is necessary to improve individual metabolic control. Further studies focusing on a stratified paediatric population to create a safe and effective algorithm, taking fat and protein into account, are necessary.