Insulin pump dosing strategies for meals varying in fat, protein or glycaemic index or grazing-style meals in type 1 diabetes: A systematic review

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.

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.

The Effect of Soy and Whey Protein Supplementation on Glucose Homeostasis in Healthy Normal Weight Asian Indians

Milk and legumes are good source of protein foods used to sustain muscle mass, but their effects on postprandial glucose homeostasis and energy metabolism may be different. This is relevant, for example, in the dietetic response to obesity or diabetes, where the intake of high-quality protein is often increased significantly. The objective of this study was to characterize the acute effect of whey and soy protein (15% vs. 30%) on glucose homeostasis, energy metabolism, and satiety. Healthy, normal body mass index (BMI) Indian adult males aged 20-35 years (n = 15) received 4 test meals (2 proteins (soy vs. whey) and 2 doses (15% vs. 30% protein: energy ratio)). Blood samples were collected serially after the meal to calculate the incremental area under the curve for plasma glucose and insulin. Energy expenditure and substrate oxidation were measured after the meal. Satiety was measured with a visual analogue scale. The insulin response, represented by the incremental area under the curve, was significantly higher for the 30% whey compared to the 30% soy protein meal (p < 0.01) but was not significantly different between the 15% protein doses. There were no differences in the plasma glucose response across protein sources or doses. The mean peak fat and carbohydrate oxidation, satiety, and energy expenditure did not differ between the protein sources and doses. In conclusion, at higher doses, whey protein has a greater insulinogenic response, compared to soy protein, and exhibits a dose-response effect. However, at lower doses, whey and soy protein elicit similar insulinogenic responses, making them equally effective protein sources in relation to glucose homoeostasis.

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.

Associations between insulin pump self-management and HbA1c in type 1 diabetes

AIMS

Insulin pump self-management is important for glycaemic outcomes. We aimed to investigate associations between self-management factors and HbA1c.

METHODS

Adult insulin pump users with type 1 diabetes (n = 770) completed an online questionnaire. The latest HbA1c and demographics were extracted from national registries. Associations between HbA1c and self-management (use of advanced features, timing of infusion set change, timing of meal bolus, data-upload and pump settings adjustments) were investigated using backward selected linear regression models.

RESULTS

Of the 699 responders eligible for this study, 60% were women; the median age and diabetes duration were 49 and 25 years, respectively. Significant associations with HbA1c were found for changing infusion set every 0-4 days relative to 5-10 days (-5 mmol/mol (-0.4%), p = 0.003), and for never/rarely missing a bolus (-6 mmol/mol (-0.5%), p < 0.001) relative to often missing a bolus. Timing insulin 10-15 min before meal relative to after meal start was also associated with lower HbA1c (-3 mmol/mol (-0.3%), p = 0.023). Self-adjusting pump settings showed the strongest association with lower HbA1c (-6 mmol/mol (-0.6%), p < 0.001) relative to health care professionals doing all adjustments.

CONCLUSION

Self-adjusting insulin pump settings, optimal timing and few omissions of meal boluses, and timely change of infusion set are associated with lower HbA1c.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2023

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, a multidisciplinary 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.

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.

Too Much Dietary Flexibility May Hinder, Not Help: Could More Specific Targets for Daily Food Intake Distribution Promote Glycemic Management among Youth with Type 1 Diabetes?

Average glycemic levels among youth with type 1 diabetes (T1D) have worsened in some parts of the world over the past decade despite simultaneous increased uptake of diabetes technology, thereby highlighting the persistent need to identify effective behavioral strategies to manage glycemia during this life stage. Nutrition is fundamental to T1D management. We reviewed the evidence base of eating strategies tested to date to improve glycemic levels among youth with T1D in order to identify promising directions for future research. No eating strategy tested among youth with T1D since the advent of flexible insulin regimens—including widely promoted carbohydrate counting and low glycemic index strategies—is robustly supported by the existing evidence base, which is characterized by few prospective studies, small study sample sizes, and lack of replication of results due to marked differences in study design or eating strategy tested. Further, focus on macronutrients or food groups without consideration of food intake distribution throughout the day or day-to-day consistency may partially underlie the lack of glycemic benefits observed in studies to date. Increased attention paid to these factors by future observational and experimental studies may facilitate identification of behavioral targets that increase glycemic predictability and management among youth with T1D.

5. Facilitating Behavior Change and Well-being to Improve Health Outcomes: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical 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, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), 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, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.