Effects of meals with different glycaemic index on postprandial blood glucose response in patients with Type 1 diabetes treated with continuous subcutaneous insulin infusion

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.

Super Bolus-A Remedy for a High Glycemic Index Meal in Children with Type 1 Diabetes on Insulin Pump Therapy?-A Randomized, Double-Blind, Controlled Trial

BACKGROUND

This study aimed to compare whether a super bolus (SB) is a more efficient strategy than a normal bolus (NB) for high glycemic index (h-GI) meals in children with type 1 diabetes (T1D).

METHODS

A randomized, double-blind, crossover trial with an allocation ratio of 1:1, registered at ClinicalTrials.gov (NCT04019821). 72 children aged 10-18 years with T1D > 1 year, and on insulin pump therapy > 3 months were included. As an intervention, they ate a h-GI breakfast for the two following days and receive a prandial insulin bolus either in the form of SB or NB.

RESULTS

The SB group had lower glucose values during the observation time and lower glucose levels in 90th min (primary end point). The median time in range was also higher after SB. At the same time, more hypoglycemic episodes and a higher time below range were noted in this group. Almost 90% of them were the threshold value for initiating treatment for hypoglycemia and occurred near the end of observation period. More hyperglycemic episodes and over twice as much time in hyperglycemia were noted after NB.

CONCLUSIONS

Super bolus is an effective strategy to avoid postprandial hyperglycemia but the basal insulin suspension should be longer to avoid hypoglycemia (f.ex. 3 h).

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.

Dietary treatment of type 1 diabetes: Beyond carbohydrate counting to fight cardiovascular risk

AIMS

Type 1 diabetes (T1D) is tied to an increased risk of cardiovascular morbidity and mortality. Dietary treatment would be an elective therapeutic strategy to fight this risk. However, it is not known what the best dietary approach is. We revisited the currently available literature on the nutritional treatment of T1D in the light of their potential comprehensive effects on the management of cardio-metabolic risk factors (body weight, fasting and postprandial glucose and lipid metabolism).

DATA SYNTHESIS

Nutritional research in T1D is mainly focused on blood glucose control, with most of the trials aiming at evaluating the acute effects of nutrients on postprandial glycemic response. The effects of the quantity and quality of nutrients and some specific foods on other metabolic risk factors have been explored mainly in cross-sectional analysis. Very few well-designed nutritional trials evaluated the best dietary approach to comprehensively manage cardiovascular risk by targeting along with blood glucose control, overweight, fasting and postprandial dyslipidemia. Therefore, the current best practice guidance for the dietary management of cardiovascular risk in T1D is generally based on evidence from patients with type 2 diabetes.

CONCLUSIONS

Well-conducted nutritional trials specifically designed for T1D are needed to identify the best dietary treatment to fight cardiovascular risk in these patients.

Postprandial glucose variability in type 1 diabetes: The individual matters beyond the meal

AIM

To explore intraindividual (between-meals) and interindividual (between-subjects) variability of postprandial glucose response (PGR) in type 1 diabetes (T1DM).

METHODS

Data were taken from five cross-over trials in 61 subjects with T1DM on insulin pump wherein the effects of different dietary components or the intraindividual-variability of PGR to the same meal were evaluated by CGM. Predictors (type of meal or nutrient composition) of early (iAUC_0-3h), late (iAUC_3-6h), total (iAUC_0-6h), and time-course of postprandial blood glucose changes (iAUC_{3-6hminus0-3h}) were evaluated using two mixed-effect linear regression models considering the patient's identification number as random-effect.

RESULTS

High-glycemic-index (HGI) and low-glycemic-index meals were the best positive and negative predictors of glucose iAUC_0-3h, respectively. A Low-Fat-HGI meal significantly predicted iAUC_{3-6hminus0-3h} (Estimate 3268; p = 0.017). Among nutrients, dietary fiber was the only significant negative predictor of iAUC_0-3h (Estimate -550; p < 0.001) and iAUC_0-6h (Estimate -742; p = 0.01) and positive predictor of iAUC_{3-6hminus0-3h} (Estimate 336; p = 0.043). For all models, the random-effect patient was statistically significant (p < 0.001 by ANOVA).

CONCLUSION

Beyond the meal characteristics (including glycemic index, fat and fiber content), individual traits significantly influence PGR. Specific interindividual factors should be further identified to properly predict glucose response to meals with different composition in individuals with T1DM.

Super Bolus: a remedy for a high glycemic index meal in children with type 1 diabetes on insulin pump therapy?-study protocol for a randomized controlled trial

Background

Postprandial hyperglycemia (PPH) is a common clinical problem among patients with type 1 diabetes (T1D), which is related to high glycemic index (h-GI) meals. The main problem is linked to high, sharp glycemic spikes following hypoglycemia after h-GI meal consumption. There is a lack of effective and satisfactory solutions for insulin dose adjustment to cover an h-GI meal. The goal of this research was to determine whether a Super Bolus is an effective strategy to prevent PPH and late hypoglycemia after an h-GI meal compared to a Normal Bolus.

Methods

A total of 72 children aged 10–18 years with T1D for at least 1 year and treated with continuous subcutaneous insulin infusion for more than 3 months will be enrolled in a double-blind, randomized, crossover clinical trial. The participants will eat a h-GI breakfast for the two following days and receive a prandial insulin bolus in the form of a Super Bolus 1 day and a Normal Bolus the next day. The glucose level 90 min after the administration of the prandial bolus will be the primary outcome measure. The secondary endpoints will refer to the glucose levels at 30, 60, 120, 150, and 180 min postprandially, the area under the blood glucose curve within 180 min postprandially, peak glucose level and the time to peak glucose level, glycemic rise, the mean amplitude of glycemic excursions, and the number of hypoglycemia episodes.

Discussion

There are still few known clinical studies on this type of bolus. A Super Bolus is defined as a 50% increase in prandial insulin dose compared to the dose calculated based on the individualized patient’s insulin-carbohydrate ratio and a simultaneous suspension of basal insulin for 2 h. Our patients reported the best experience with such a combination. A comprehensive and effective solution to this frequent clinical difficulty of PPH after an h-GI meal has not yet been found. The problem is known and important, and the presented solution is innovative and easy to apply in everyday life.

Trial registration

ClinicalTrials.gov NCT04019821

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.

Dietary Changes During COVID-19 Lockdown in Adults With Type 1 Diabetes on a Hybrid Artificial Pancreas

In this retrospective analysis, we examine the impact of the lockdown of the coronavirus pandemic (COVID-19) on eating habits in individuals with type 1 diabetes (T1D) on a hybrid artificial pancreas (HAP). Dietary composition before and during lockdown was assessed by 7-day food records of 12 participants with T1D on HAP (three men and nine women, ages 38 ± 13 years, HbA1c 6.8 ± 0.3%, M ± SD). Continuous glucose monitoring (CGM) metrics and lifestyle changes (online questionnaire) were also assessed. Compared to prelockdown, reported body weight tended to increase during lockdown with no changes in total energy intake. Participants significantly decreased animal protein intake (-2.1 ± 3.7% of total energy intake, p = 0.048), but tended to increase carbohydrate intake (+17 ± 28 g/day, p = 0.052). These changes were induced by modifications of eating habits at breakfast and lunch during weekdays. Patients consumed more cereals (+21 ± 33 g/day, p = 0.038), whole grain (+22 ± 32 g/day, p = 0.044), and sweets (+13 ± 17 g/day, p = 0.021), and less animal protein sources (-42 ± 67 g/day, p = 0.054). Participants showed a more regular meal timing and decreased physical activity. Blood glucose control remained optimal (time-in-range 76 ± 8 vs. 75 ± 7% before lockdown), and daily total insulin infusion increased (42 ± 10 vs. 39 ± 12 I.U., p = 0.045). During the lockdown, patients with T1D on HAP modified dietary habits by decreasing animal protein and increasing carbohydrate intake. This increase, mainly concerning whole grain and low-glycemic-index products, did not influence blood glucose control.

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.

The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.

Glycemic Index, Extended Bolusing, and Diabetes Education in Insulin Pump Therapy (GLIDE: A Pilot Study)

Background: There is no published evidence on whether advanced bolus education affects outcomes in insulin pump-treated type 1 diabetes. We assess the feasibility of delivering a clinical education program on rates of digestion and bolusing, and to assess its preliminary impact. Methods: An interactive education session on glycemic index (GI), extended bolusing, and superbolusing was developed and assessed in a nonrandomized single-arm study for 12 weeks. Insulin pump-treated participants with type 1 diabetes were recruited. Glucose outcomes were assessed by blinded continuous glucose monitoring after the consumption of high-fat and high-GI test meal. The primary outcome measure was 8-h glucose area under the curve (AUC) after high-fat meals, before and after intervention. Secondary outcomes included time spent in hypoglycemia, quality of life, treatment satisfaction, HbA1c, frequency of use of extended boluses, and postprandial AUC. Results: Eleven participants completed the study [mean (SD) age 42.3 (12.8) years, baseline HbA1c 57.3 (10.0) mmol/mol, duration of diabetes 19.5 (9.9) years]. AUC for glucose after test meals did not differ significantly after education except for in the first 2 h after the high-GI meal [precourse 83.1 (0.23-88.9), postcourse 5.38 (-16.2 to 50.8)]. Percentage time spent in hypoglycemia (<3.9 and <3.3 mmol/L) fell at week 12 compared with baseline [5.8 (IQR 2.1-8.3) % to 4.3 (IQR 2.1-5.4) %, P = 0.013, and 2.9 (IQR 1.2-3.9) % to 1.6 (IQR 0.7-2.4) %, P = 0.029, respectively]. Conclusion: Delivering an education program to support advanced boluses is feasible and may reduce exposure to hypoglycemia. A further trial is required to confirm the findings.

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.

Fibre-enriched buckwheat pasta modifies blood glucose response compared to corn pasta in individuals with type 1 diabetes and celiac disease: Acute randomized controlled trial

AIM

People with type 1 diabetes and celiac disease (T1D&CD) have high blood glucose variability. Processed gluten-free foods have shown to induce a worse metabolic profile whereas naturally gluten-free foods may represent healthier options. On the other hand, dietary fibre has shown to reduce postprandial glycemic excursions in individuals with diabetes. Thus, we evaluated the acute effect of fibre-enriched buckwheat (FBP) and corn pasta (CP) on postprandial blood glucose response (PP-BGR).

METHODS

Ten adult patients with T1D&CD consumed two meals with the same amount of carbohydrate while differing only for pasta type (FBP or CP) preceded by the same insulin bolus. Participants utilized continuous glucose monitoring (CGM) and data over 6 h after meal were analyzed.

RESULTS

PP-BGR differed between the two meals, being significantly lower in the first period (0-3 h) after the CP than the FBP meal (iAUC: -38 ± 158 vs. 305 ± 209 mmol/L · 180 min, p = 0.040), whereas significantly higher in the second period (3-6 h) after the CP than the FBP meal (iAUC: 432 ± 153 vs. 308 ± 252 mmol/L · 180 min, p = 0.030). Overall, a less variable postprandial profile was observed after FBP than CP consumption.

CONCLUSIONS

In individuals with T1D&CD, the acute consumption of FBP induces significant differences in PP-BGR compared with CP that may be clinically relevant.

Metabolic response to amylose-rich wheat-based rusks in overweight individuals

BACKGROUND/OBJECTIVES

The amylose-amylopectin ratio influences starch properties. A higher amylose content is associated with slower starch digestion thus reducing the postprandial plasma glucose response and improving the overall postprandial metabolism. So far, limited evidence is available on the metabolic effect of wheat-based foods rich in amylose. This randomised controlled study investigated the acute metabolic effects of amylose-rich wheat-based rusks in overweight subjects focusing on potential mechanisms.

SUBJECTS/METHODS

Ten overweight subjects consumed in random order two test meals differing only in the carbohydrate source: rusks prepared with amylose-rich wheat flour (ARR) or conventional wheat flour (control). Blood samples were taken at fasting and over 4 h after the meal. Satiety and intestinal fermentation were evaluated by VAS and H2-breath test, respectively.

RESULTS

ARR reduced plasma glucose response during the first two hours after the meal and the desire to eat, and increased breath hydrogen concentration at 4 h (p < 0.05 for all). Moreover, according to computational models, the ARR slightly reduced intestinal glucose absorption in the first hour after the meal and increased the overall postprandial insulin sensitivity.

CONCLUSIONS

Rusks made with amylose-rich flour could be useful for improving postprandial glucose metabolism and reduce the desire to eat, thus possibly contributing to the prevention and treatment of overweight/obesity, impaired glucose tolerance or diabetes.

Diabetes remission after bariatric surgery is characterized by high glycemic variability and high oxidative stress

BACKGROUND AND AIM

To evaluate glycemic variability (GV) and oxidative stress in patients who achieved type 2 diabetes (T2DM) remission after bariatric surgery (BS).

METHODS AND RESULTS

Twenty-two patients (M/F10/12, age 50 ± 9 years, BMI 31 ± 6 kg/m²) who were in remission of T2DM (T2DM remitters) after BS since at least 1 year and 22 age-, sex- and BMI-matched control subjects were studied. Of the BS group, eleven subjects had undergone Roux-en-Y gastric bypass (RYGB) and eleven subjects sleeve gastrectomy (SG). Oral glucose tolerance test (OGTT), 7 days-continuous glucose monitoring, 24-h urinary excretion of 8-isoprostaglandin F2α (8-isoPGF2α) and dietary intake evaluation were performed. According to general linear model for repeated measures, glucose and insulin response during OGTT were significantly different in T2DM remitter than in control subjects (p < 0.001, for both). All measures of GV (standard deviation, coefficient of variation and mean amplitude of glucose excursions) were significantly higher in T2DM remitters than in controls, (p < 0.001 for all). These indexes were higher among RYGB than SG patients (p < 0.05). The time spent out of the 60-160 mg/dl range was significantly longer in T2DM remitters undergoing RYGB than in controls (p < 0.02). Mean 24-h urinary 8-isoPGF2α excretion was significantly higher in T2DM remitters than that of control subjects (p = 0.04). All GV indexes were directly correlated with blood glucose levels at 30 and 60 min during OGTT (p < 0.05-0.001).

CONCLUSION

Remission of T2DM after BS is characterized by high GV and high oxidative stress in the face of fasting blood glucose and HbA1c within the normal range.

Weight Management in Patients with Type 1 Diabetes and Obesity

PURPOSE OF REVIEW

Patients with type 1 diabetes (T1D) are typically viewed as lean individuals. However, recent reports showed that their obesity rate surpassed that of the general population. Patients with T1D who show clinical signs of type 2 diabetes such as obesity and insulin resistance are considered to have "double diabetes." This review explains the mechanisms of weight gain in patients with T1D and how to manage it.

RECENT FINDINGS

Weight management in T1D can be successfully achieved in real-world clinical practice. Nutrition therapy includes reducing energy intake and providing a structured nutrition plan that is lower in carbohydrates and glycemic index and higher in fiber and lean protein. The exercise plan should include combination stretching as well as aerobic and resistance exercises to maintain muscle mass. Dynamic adjustment of insulin doses is necessary during weight management. Addition of anti-obesity medications may be considered. If medical weight reduction is not achieved, bariatric surgery may also be considered.

Accurate Carbohydrate Counting Is an Important Determinant of Postprandial Glycemia in Children and Adolescents With Type 1 Diabetes on Insulin Pump Therapy

BACKGROUND

Carbohydrate (CHO) counting is a key nutritional intervention utilized in the management of diabetes to optimize postprandial glycemia. The aim of the study was to examine the impact of accuracy of CHO counting on the postprandial glucose in children and adolescents with type 1 diabetes on insulin pump therapy.

METHODS

Children/adolescents with type 1 diabetes who were on insulin pump therapy for a minimum of 6 months are enrolled in the study. Patients were instructed to record details of meals consumed, estimated CHO count per meal, and 2-hour postprandial glucose readings over 3-5 days. Meals' CHO contents were recounted by an experienced clinical dietician, and those within 20% of the dietician's counting were considered accurate.

RESULTS

A total of 30 patients (21 females) were enrolled. Age range (median) was 8-18 (SD 13) years. Data of 247 meals were analyzed. A total of 165 (67%) meals' CHO contents were accurately counted. Of those, 90 meals (55%) had in-target postprandial glucose ( P < .000). There was an inverse relationship between inaccurate CHO estimates and postprandial glucose. Of the 63 underestimated meals, 55 had above-target glucose, while 12 of the 19 overestimated meals were followed by low glucose. There was no association between accuracy and meal size (Spearman's rho = .019).

CONCLUSION

Accuracy of CHO counting is an important determining factor of postprandial glycemia. However, other factors should be considered when advising on prandial insulin calculation. Underestimation and overestimation of CHO result in postprandial hyperglycemia and hypoglycemia, respectively. Accuracy does not correlate with meal size.

Micronutrient Intake in a Cohort of Italian Adults with Type 1 Diabetes: Adherence to Dietary Recommendations

Micronutrients are of fundamental importance in maintaining health status. However, data on their dietary intake are few particularly in persons with diabetes. The aim of this study was to evaluate in adults with type 1 diabetes (T1DM) attending a tertiary-level diabetes center in Southern Italy the intake of micronutrients (both vitamins and minerals) and the adherence to recommendations. Seven-day food records of 60 T1DM patients were analyzed. Micronutrient intake was evaluated based on the Italian food composition tables and expressed as amount per 1000 kcal of energy intake to adjust for possible underreporting. Adherence to recommendations for vitamins A, B₆, B₁₂, and C and niacin was acceptable in both sexes (ranging from 77% to 100%). Half of the patients did not adhere to folate recommendation, even less to vitamin E, and no patient reached the recommended intake for vitamin D. As for minerals, adherence was low for potassium and selenium (0-23%); intermediate for zinc, copper, and magnesium; low and intermediate for calcium in men and women, respectively; and low for iron in women. In conclusion, the diet followed by T1DM patients may not have a sufficient content of different micronutrients. Therefore, an adequate intake of low-fat dairy products, fish, legumes, and vegetables should be encouraged as components of a healthier dietary pattern.

Greater diet quality is associated with more optimal glycemic control in a longitudinal study of youth with type 1 diabetes

BACKGROUND

Despite the centrality of nutrition in the management of type 1 diabetes, the association of diet quality and macronutrient distribution with glycemic control is ambiguous.

OBJECTIVE

This study examined longitudinally the association of dietary intake with multiple indicators of glycemic control in youth with type 1 diabetes participating in a behavioral nutrition intervention study.

DESIGN

Participants in a randomized clinical trial of a behavioral nutrition intervention [n = 136; mean ± SD age: 12.8 ± 2.6 y; glycated hemoglobin (HbA1c): 8.1% ± 1.0%; 69.1% using an insulin pump] completed 3-d diet records at baseline and months 3, 6, 9, 12, and 18; masked continuous glucose monitoring (CGM) data were obtained concurrently with the use of the Medtronic iPro CGM system. HbA1c was obtained every 3 mo; 1,5-anhydroglucitol was obtained every 6 mo. Linear mixed-effects regression models estimated associations of time-varying dietary intake variables with time-varying glycemic control indicators, controlling for age, height, weight, sex, Tanner stage, diabetes duration, regimen, frequency of blood glucose monitoring, physical activity, and treatment assignment.

RESULTS

HbA1c was associated inversely with carbohydrate and natural sugar, and positively with protein and unsaturated fat. 1,5-Anhydroglucitol was associated positively with fiber intake and natural sugar. Greater glycemic control as indicated by ≥1 CGM variable was associated with higher Healthy Eating Index-2005, whole plant food density, fiber, carbohydrate, and natural sugar and lower glycemic index and unsaturated fat.

CONCLUSIONS

Both overall diet quality and macronutrient distribution were associated with more optimal glycemic control. Associations were more consistent for CGM variables obtained concurrently with dietary intake than for biomarkers of longer-term glycemic control. These findings suggest that glycemic control may be improved by increasing intake of high-fiber, low glycemic-index, carbohydrate-containing foods. This trial was registered at clinicaltrials.gov as NCT00999375.

Extra-Virgin Olive Oil Reduces Glycemic Response to a High-Glycemic Index Meal in Patients With Type 1 Diabetes: A Randomized Controlled Trial

OBJECTIVE

To evaluate whether fat quality, in the context of meals with high- (HGI) or low-glycemic index (LGI), influences postprandial blood glucose (PPG) response in patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

According to a randomized crossover design, 13 patients with type 1 diabetes on insulin pump consumed two series (HGI or LGI) of meals with the same carbohydrate quantity while differing for amount and quality of fat: 1) low in fat ("low fat"), 2) high in saturated fat (butter), or 3) high in monounsaturated fat (extra-virgin olive oil) (EVOO). Premeal insulin doses were based on insulin-to-glycemic load ratios. Continuous glucose monitoring was performed and 6-h PPG evaluated.

RESULTS

PPG was significantly different between HGI and LGI meals (P = 0.005 for time × glycemic index interaction by repeated-measures analysis [RMA]), being significantly higher during the first 3 h after the HGI meals with a later tendency to an opposite pattern. In the context of HGI meals, PPG was significantly lower after EVOO than after low fat or butter (P < 0.0001 for time × meal interaction by RMA), with a marked difference in the 0- to 3-h glucose incremental area under the curve between EVOO (mean ± SD 198 ± 274 mmol/L × 180 min) and either low fat (416 ± 329) or butter (398 ± 355) (P < 0.05). No significant differences were observed in PPG between the three LGI meals.

CONCLUSIONS

Carbohydrate quality of a mixed meal influences shape and extent of PPG. Besides, using EVOO in a HGI meal attenuates the early postprandial glucose response observed when this meal is consumed with either low fat or butter. Therefore, an optimal prandial insulin administration would require considering, in addition to the quantity of carbohydrates, the quality of both carbohydrate and fat.

The relationship between carbohydrate and the mealtime insulin dose in type 1 diabetes

A primary focus of the nutritional management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. Different methods exist to quantify carbohydrate including counting in one gram increments, 10g portions or 15g exchanges. Clinicians have assumed that counting in one gram increments is necessary to precisely dose insulin and optimize postprandial control. Carbohydrate estimations in portions or exchanges have been thought of as inadequate because they may result in less precise matching of insulin dose to carbohydrate amount. However, studies examining the impact of errors in carbohydrate quantification on postprandial glycemia challenge this commonly held view. In addition it has been found that a single mealtime bolus of insulin can cover a range of carbohydrate intake without deterioration in postprandial control. Furthermore, limitations exist in the accuracy of the nutrition information panel on a food label. This article reviews the relationship between carbohydrate quantity and insulin dose, highlighting limitations in the evidence for a linear association. These insights have significant implications for patient education and mealtime insulin dose calculations.

Glycaemic load versus carbohydrate counting for insulin bolus calculation in patients with type 1 diabetes on insulin pump

AIMS

To evaluate feasibility and effectiveness on short-term blood glucose control of using glycaemic load counting (GLC) versus carbohydrate counting (CC) for prandial insulin dosing in patients with type 1 diabetes (T1D).

METHODS

Nine T1D patients on insulin pump, aged 26-58 years, HbA1c 7.7 ± 0.8 % (61 ± 8.7 mmol/mol), participated in this real-life setting study. By a crossover design, patients were randomised to calculate their pre-meal insulin dose based on the insulin/glycaemic load ratio (GLC period) or the insulin/carbohydrate ratio (CC period) for 1 week, shifting to the alternate method for the next week, when participants duplicated their first week food plan. Over either week, a blind subcutaneous continuous glucose monitoring was performed, and a 7-day food record was filled in.

RESULTS

Total daily insulin doses (45 ± 10 vs. 44 ± 9 I.U.; M ± SD, p = 0.386) and basal infusion (26 ± 7 vs. 26 ± 8 I.U., p = 0.516) were not different during GLC and CC periods, respectively. However, the range of insulin doses (difference between highest and lowest insulin dose) was wider during GLC, with statistical significance at dinner (8.4 ± 6.2 vs. 6.0 ± 3.9 I.U., p = 0.041). Blood glucose iAUC after lunch was lower, albeit not significantly, during GLC than CC period (0.6 ± 8.6 vs. 3.4 ± 8.2 mmol/l∙3 h, p = 0.059). Postprandial glucose variability, evaluated as the maximal amplitude after meal (highest minus lowest glucose value), was significantly lower during GLC than CC period at lunch (4.22 ± 0.28 vs. 5.47 ± 0.39 mmol/l, p = 0.002) and dinner (3.89 ± 0.33 vs. 4.89 ± 0.33, p = 0.026).

CONCLUSIONS

Calculating prandial insulin bolus based on glycaemic load counting is feasible in a real-life setting and may improve postprandial glucose control in people with T1D.

Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC)

BACKGROUND AND AIMS

The positive and negative health effects of dietary carbohydrates are of interest to both researchers and consumers.

METHODS

International experts on carbohydrate research held a scientific summit in Stresa, Italy, in June 2013 to discuss controversies surrounding the utility of the glycemic index (GI), glycemic load (GL) and glycemic response (GR).

RESULTS

The outcome was a scientific consensus statement which recognized the importance of postprandial glycemia in overall health, and the GI as a valid and reproducible method of classifying carbohydrate foods for this purpose. There was consensus that diets low in GI and GL were relevant to the prevention and management of diabetes and coronary heart disease, and probably obesity. Moderate to weak associations were observed for selected cancers. The group affirmed that diets low in GI and GL should always be considered in the context of diets otherwise understood as healthy, complementing additional ways of characterizing carbohydrate foods, such as fiber and whole grain content. Diets of low GI and GL were considered particularly important in individuals with insulin resistance.

CONCLUSIONS

Given the high prevalence of diabetes and pre-diabetes worldwide and the consistency of the scientific evidence reviewed, the expert panel confirmed an urgent need to communicate information on GI and GL to the general public and health professionals, through channels such as national dietary guidelines, food composition tables and food labels.

Coefficient of variation of R-R interval closely correlates with glycemic variability assessed by continuous glucose monitoring in insulin-depleted patients with type 1 diabetes

AIMS

In type 1 diabetic patients, insulin secretory capacity, meals and physical activity correlate with glycemic variability. Autonomic function associated with gastrointestinal motility and counterregulatory hormone secretion is another candidate which correlates with glucose variability. The aim of this study is to clarify a new clinical parameter associated with glycemic variability in insulin-depleted patients with type 1 diabetes.

METHODS

We studied 31 inpatients with type 1 diabetes. We evaluated glycemic variability calculated by continuous glucose monitoring, clinical parameters and the coefficient of variation of R-R interval (CVR-R). Glycemic variability was also assessed during the daytime and nighttime.

RESULTS

The CVR-R showed a significant negative correlation with the whole-day standard deviation (SD) (r = -0.50, p = 0.007), mean amplitude of glycemic excursions (MAGE) (r = -0.47, p=0.011), M-value (r = -0.38, p = 0.048) and mean of daily differences (MODD) (r = -0.59, p = 0.001). The CVR-R also showed a significant negative correlation with the nighttime SD (r = -0.59, p = 0.001), MAGE (r = -0.47, p=0.011), M-value (r = -0.53, p = 0.004) and MODD (r = -0.65, p = 0.0003). And furthermore, the CVR-R also showed a significant negative correlation with the daytime SD (r = -0.44, p = 0.019) and MAGE (r = -0.50, p = 0.006), but not with the daytime M-value or MODD. The nighttime SD was significantly higher in patients with diabetic polyneuropathy than in patients without it (p = 0.016), while the CVR-R was significantly lower in patients with polyneuropathy than in patients without it (p = 0.009).

CONCLUSIONS

CVR-R is closely correlated with glycemic variability, especially during nighttime, in insulin-depleted patients with type 1 diabetes. Measuring CVR-R may help us to presume the degree of glycemic variability in those patients.

Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era

BACKGROUND

Continuous glucose monitoring highlights the complexity of postprandial glucose patterns present in type 1 diabetes and points to the limitations of current approaches to mealtime insulin dosing based primarily on carbohydrate counting.

METHODS

A systematic review of all relevant biomedical databases, including MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials, was conducted to identify research on the effects of dietary fat, protein, and glycemic index (GI) on acute postprandial glucose control in type 1 diabetes and prandial insulin dosing strategies for these dietary factors.

RESULTS

All studies examining the effect of fat (n = 7), protein (n = 7), and GI (n = 7) indicated that these dietary factors modify postprandial glycemia. Late postprandial hyperglycemia was the predominant effect of dietary fat; however, in some studies, glucose concentrations were reduced in the first 2-3 h, possibly due to delayed gastric emptying. Ten studies examining insulin bolus dose and delivery patterns required for high-fat and/or high-protein meals were identified. Because of methodological differences and limitations in experimental design, study findings were inconsistent regarding optimal bolus delivery pattern; however, the studies indicated that high-fat/protein meals require more insulin than lower-fat/protein meals with identical carbohydrate content.

CONCLUSIONS

These studies have important implications for clinical practice and patient education and point to the need for research focused on the development of new insulin dosing algorithms based on meal composition rather than on carbohydrate content alone.

A low-glycemic index meal and bedtime snack prevents postprandial hyperglycemia and associated rises in inflammatory markers, providing protection from early but not late nocturnal hypoglycemia following evening exercise in type 1 diabetes

OBJECTIVE

To examine the influence of the glycemic index (GI) of foods consumed after evening exercise on postprandial glycemia, metabolic and inflammatory markers, and nocturnal glycemic control in type 1 diabetes.

RESEARCH DESIGN AND METHODS

On two evenings (∼1700 h), 10 male patients (27 ± 5 years of age, HbA1c 6.7 ± 0.7% [49.9 ± 8.1 mmol/mol]) were administered a 25% rapid-acting insulin dose with a carbohydrate bolus 60 min before 45 min of treadmill running. At 60 min postexercise, patients were administered a 50% rapid-acting insulin dose with one of two isoenergetic meals (1.0 g carbohdyrate/kg body mass [BM]) matched for macronutrient content but of either low GI (LGI) or high GI (HGI). At 180 min postmeal, the LGI group ingested an LGI snack and the HGI group an HGI snack (0.4 g carbohdyrate/kg BM) before returning home (∼2300 h). Interval samples were analyzed for blood glucose and lactate; plasma glucagon, epinephrine, interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α); and serum insulin, cortisol, nonesterified fatty acid, and β-hydroxybutyrate concentrations. Interstitial glucose was recorded for 20 h postlaboratory attendance through continuous glucose monitoring.

RESULTS

Following the postexercise meal, an HGI snack induced hyperglycemia in all patients (mean ± SD glucose 13.5 ± 3.3 mmol/L) and marked increases in TNF-α and IL-6, whereas relative euglycemia was maintained with an LGI snack (7.7 ± 2.5 mmol/L, P < 0.001) without inflammatory cytokine elevation. Both meal types protected all patients from early hypoglycemia. Overnight glycemia was comparable, with a similar incidence of nocturnal hypoglycemia (n = 5 for both HGI and LGI).

CONCLUSIONS

Consuming LGI food with a reduced rapid-acting insulin dose following evening exercise prevents postprandial hyperglycemia and inflammation and provides hypoglycemia protection for ∼8 h postexercise; however, the risk of late nocturnal hypoglycemia remains.

Diabetes education: the experiences of young adults with type 1 diabetes

INTRODUCTION

Clinician-led diabetes education is a fundamental component of care to assist people with Type 1 diabetes (T1D) self-manage their disease. Recent initiatives to incorporate a more patient-centered approach to diabetes education have included recommendations to make such education more individualized. Yet there is a dearth of research that identifies patients' perceptions of clinician-led diabetes education. We aimed to describe the experience of diabetes education from the perspective of young adults with T1D.

METHODS

We designed a self-reported survey for Australian adults, aged 18-35 years, with T1D. Participants (n = 150) were recruited by advertisements through diabetes consumer-organizations. Respondents were asked to rate aspects of clinician-led diabetes education and identify sources of self-education. To expand on the results of the survey we interviewed 33 respondents in focus groups.

RESULTS

SURVEY

The majority of respondents (56.0%) were satisfied with the amount of continuing clinician-led diabetes education; 96.7% sought further self-education; 73.3% sourced more diabetes education themselves than that provided by their clinicians; 80.7% referred to diabetes organization websites for further education; and 30.0% used online chat-rooms and blogs for education. Focus groups: The three key themes that emerged from the interview data were deficiencies related to the pedagogy of diabetes education; knowledge deficiencies arising from the gap between theoretical diabetes education and practical reality; and the need for and problems associated with autonomous and peer-led diabetes education.

CONCLUSION

Our findings indicate that there are opportunities to improve clinician led-diabetes education to improve patient outcomes by enhancing autonomous health-literacy skills and to incorporate peer-led diabetes education and support with clinician-led education. The results provide evidence for the potential value of patient engagement in quality improvement and health-service redesign.

Shared decision-making: the perspectives of young adults with type 1 diabetes mellitus

BACKGROUND

Shared decision-making (SDM) is at the core of patient-centered care. We examined whether young adults with type 1 diabetes perceived the clinician groups they consulted as practicing SDM.

METHODS

In a web-based survey, 150 Australians aged 18-35 years and with type 1 diabetes rated seven aspects of SDM in their interactions with endocrinologists, diabetes educators, dieticians, and general practitioners. Additionally, 33 participants in seven focus groups discussed these aspects of SDM.

RESULTS

Of the 150 respondents, 90% consulted endocrinologists, 60% diabetes educators, 33% dieticians, and 37% general practitioners. The majority of participants rated all professions as oriented toward all aspects of SDM, but there were professional differences. These ranged from 94.4% to 82.2% for "My clinician enquires about how I manage my diabetes"; 93.4% to 82.2% for "My clinician listens to my opinion about my diabetes management"; 89.9% to 74.1% for "My clinician is supportive of my diabetes management"; 93.2% to 66.1% for "My clinician suggests ways in which I can improve my self-management"; 96.6% to 85.7% for "The advice of my clinician can be understood"; 98.9% to 82.2% for "The advice of my clinician can be trusted"; and 86.5% to 67.9% for "The advice of my clinician is consistent with other members of the diabetes team". Diabetes educators received the highest ratings on all aspects of SDM. The mean weighted average of agreement to SDM for all consultations was 84.3%. Focus group participants reported actively seeking clinicians who practiced SDM. A lack of SDM was frequently cited as a reason for discontinuing consultation. The dominant three themes in focus group discussions were whether clinicians acknowledged patients' expertise, encouraged patients' autonomy, and provided advice that patients could utilize to improve self-management.

CONCLUSION

The majority of clinicians engaged in SDM. Young adults with type 1 diabetes prefer such clinicians. They may fail to take up recommended health services when clinicians do not practice this component of patient-centered care. Such findings have implications for patient safety, improved health outcomes, and enhanced health service delivery.

Glycemic index, glycemic load and childhood obesity: A systematic review

Background:

Several evidences have been reported so far in terms of the relationship between obesity and glycemic index and glycemic load in children. However, the number of review studies that have dealt with recent findings is quite low. The purpose of present study is to review the existing evidences in this regard.

Materials and Methods:

First of all, the phrases: “Glycaemic index”, “Glycaemic load”, “Glycemic index” OR “Glycemic load” accompanied by one of the words: “Adolescent”, “Young”, “Youth” “Children” OR “Child” were searched in texts of articles existing in ISI and PUBMED databases which were obtained out of 1001 articles. Among these, some articles, which reviewed the relationship of obesity with glycemic index and glycemic load, were selected. Finally, 20 articles were studied in current review study.

Results:

The majority of cross-sectional studies have found children's obesity directly linked with glycemic index and glycemic load; however, cohort studies found controversial results. Also, the intervention studies indicate the negative effect of glycemic index and glycemic load on obesity in children.

Conclusion:

Published evidences reported inconsistent results. It seems that existing studies are not sufficient and more studies are needed in this regard.

Dietary carbohydrates for diabetics

The literature on the impact of dietary carbohydrates in the regulation of blood glucose levels and other metabolic abnormalities in diabetic patients over the last 3 years is reviewed. We try to differentiate the metabolic effects due to the amount of carbohydrates from those due to their different types. The review comprises a part dealing with the effects of diets having low or high carbohydrate content on body weight reduction, and a part in which the amount and the quality of carbohydrates are discussed in relation to isoenergetic diets. Overall, the data accumulated in the period considered seem to confirm that the decrease in energy intake is more important than the qualitative composition of the diet to reduce body weight, but that both the amount and the quality of carbohydrates are important in modulating blood glucose levels and other cardiovascular risk factors in both the fasting and the postprandial phases in diabetic individuals.

Blood glucose levels in children with Type 1 diabetes attending a residential diabetes camp: a 2-year review

AIMS

The objective of this retrospective analysis of blood glucose values at a week-long residential summer camp for children with Type 1 diabetes was to provide experiential data to reinforce current summer camp guidelines and to determine if specific interventions implemented between 2009 and 2010 were effective in lowering average blood glucose among our campers without increasing risk of hypoglycaemia.

METHODS

Blood glucose records were obtained from a random selection of children who attended six 1-week camp sessions, three each in 2009 and 2010. Five values per day: pre-meal breakfast, lunch and dinner, pre-evening snack and midnight values were analysed.

RESULTS

A total of 13,267 blood glucose values were included. There were no severe hypoglycaemic episodes, seizures or need for full-dose glucagon or intravenous glucose in either year. Mean blood glucose was significantly lower in 2010 compared with 2009 (9.22 vs. 10.06 mmol/l, P < 0.001). Older age and camp year were associated with lower mean blood glucose, even when controlling for gender and duration of diabetes.

CONCLUSIONS

This analysis is the largest so far conducted at a residential diabetes camp. Mean blood glucose levels were lower than other published studies. Although we cannot attribute a cause-and-effect relationship between our interventions and the improvement in blood glucose between 2009 and 2010, the use of pre-meal insulin bolus doses, low glycaemic meals and highlighting blood glucose levels in logs before being reviewed by endocrinologists are strongly encouraged. From this study we hope to establish benchmarks for comparison among camps and begin to identify best practices.